object(Publication)#694 (6) { ["_data"]=> array(29) { ["id"]=> int(8603) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:39" ["primaryContactId"]=> int(11056) ["sectionId"]=> int(59) ["seq"]=> int(1) ["submissionId"]=> int(8478) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(2502) "BRANCO, Sérgio – FERREIRA, André G.– CABRAL, Jorge (2019): Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey. Electronics, 8(11). Online: https://doi.org/10.3390/electronics8111289 DarwinFPV 1.2G 1.6W VTX [s. a.]. Online: https://darwinfpv.com/products/darwinfpv-fpv-drone-replaces-matek-1-2g-1-3g-1-6w-vtx Eachine TX805S Transmitter Product Instruction Manual [s. a.]. Online: https://manuals.plus/wp-content/sideloads/eachine-tx805s-transmitter-manual-original.pdf Ettus USRP B210 [s. a.]. Online: https://www.ettus.com/all-products/ub210-kit/ FARKAS, Gábor (2024): SDR-adatfolyam feldolgozása korszerű módszerekkel. Hadmérnök, 19(2), 87–95. Online: https://doi.org/10.32567/hm.2024.2.7 FARKAS, Gábor – FAZEKAS, Gábor – NÉMETH, András (2025): FPV-drónok detektálásának alternatív megoldása konvolúciós neurális hálózattal. Haditechnika, 59(2), 2–7. Online: http://doi.org/10.23713/HT.59.2.01 HackRF documentation. Online: https://hackrf.readthedocs.io/en/latest/index.html HAIG, Zsolt (2021): Relationships between Cyberspace Operations and Information Operations. Advances in Military Technology, 16(1), 91–105. Online: https://doi.org/10.3849/aimt.01466 HumbirdTec VTX-1G3TE [s. a.]. Online: https://prom.ua/m-6870578947007456428-fpv-videoperedatchik-humbirdtec.html NÉMETH, András – VIRÁGH, Krisztián (2023): Mesterséges intelligencia és haderő – További katonai alkalmazási lehetőségek VIII. rész. Haditechnika, 57(2), 2–5. Online: https://doi.org/10.23713/HT.57.2.01 OLLOY, Oleksandra (2024): Drones in Modern Warfare: Lessons Learnt from the War in Ukraine. Australian Army Occasional Paper No. 29. Online: https://doi.org/10.61451/267513 RUSHFPV Tank Solo User Manual [s. a.]. Online: https://distributions.com.ua/files/Istrukciya_RUSH-DA14_Pryami_dysstrybucii.pdf RTL-SDR product page. Online: https://www.rtl-sdr.com/about-rtl-sdr/ ȘORECĂU, Mirela et al. (2025): Enhanced RF Spectrum Monitoring with SDR-Based Frequency-Sweep Methods. 2025 International Symposium on Electromagnetic Compatibility – EMC Europe, Paris, France. Online: https://doi.org/10.1109/EMCEurope61644.2025.11176413 TBS Unify Pro 5G8 (HV) Video Transmitter (2018). Online: https://www.team-blacksheep.com/media/files/tbs-unify-pro-5g8-manual.pdf ZHANG, Qianru et al. (2019): Recent Advances in Convolutional Neural Network Acceleration. Neurocomputing, 323, 37–51. Online: https://doi.org/10.1016/j.neucom.2018.09.038" ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(4) "5-16" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.1" ["abstract"]=> array(1) { ["en_US"]=> string(1084) "

The increasing dependence of modern societies and military operations on radio frequency-based and networked systems has made the electromagnetic spectrum a critical operational domain. Electronic warfare capabilities must therefore evolve toward more adaptive and rapidly deployable solutions. This article addresses the challenge of accelerating electronic warfare related research and development by introducing a compact, modular framework that enables efficient testing and validation of signal processing and machine learning-based detection algorithms in real-world conditions. To achieve this, comparison of possible technologies and architecture was made to select the optimal components. The proposed system combines a software-defined radio and an embedded processing unit to create a field-deployable platform for radio frequency signal collection, analysis and countermeasure evaluation. The framework’s functionality was demonstrated through an FPV drone detection use case, where video signals transmitted by a drone were successfully identified and disrupted.

" } ["subtitle"]=> array(1) { ["en_US"]=> string(50) "An Approach to Accelerate Research and Development" } ["title"]=> array(1) { ["en_US"]=> string(28) "Electronic Warfare Framework" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(13) "Farkas Gábor" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#731 (6) { ["_data"]=> array(15) { ["id"]=> int(11056) ["email"]=> string(26) "farkas.gabor.csp@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8603) ["seq"]=> int(1) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0009-0005-2463-6876" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(36) "a:1:{s:5:"hu_HU";s:10:"NKE - KMDI";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(6) "Farkas" ["hu_HU"]=> string(6) "Farkas" } ["givenName"]=> array(2) { ["en_US"]=> string(6) "Gábor" ["hu_HU"]=> string(6) "Gábor" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(6) { [0]=> string(18) "Electronic warfare" [1]=> string(6) "SIGINT" [2]=> string(22) "Software-defined radio" [3]=> string(16) "Machine learning" [4]=> string(4) "CUAV" [5]=> string(9) "FPV drone" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#744 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46142) ["id"]=> int(6783) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8603) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#124 (6) { ["_data"]=> array(29) { ["id"]=> int(8580) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:40" ["primaryContactId"]=> int(11020) ["sectionId"]=> int(59) ["seq"]=> int(2) ["submissionId"]=> int(8455) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(12664) "BAJAK, Frank (2023): Pentagon’s AI initiatives Accelerate Hard Decisions on Lethal Autonomous Weapons. AP News, 25 November 2023. Online: https://apnews.com/article/us-military-ai-projects-0773b4937801e7a0573f44b57a9a5942 BEZOMBES, Patrick – BRUNESSAUX, Stéphan – CADZOW, Scott (2023): Cybersecurity of AI and Standardisation. European Union Agency for Cybersecurity (ENISA). Online: https://doi.org/10.2824/277479 DefenseMirror.com (2025): Saab Tests AI co-Pilot in Gripen Fighter Jet for First Time. DefenseMirror.com, 11 June 2025. Online: https://www.defensemirror.com/news/39650 ERDÉSZ, Viktor (2023): A mesterséges intelligencia alkalmazása a katonai nemzetbiztonsági hírszerzésben [The Use of Artificial Intelligence in Military National Security Intelligence]. Budapest: Katonai Nemzetbiztonsági Szolgálat. ERŐS, Hunor (2025): A mesterséges intelligencia a magyar honvédségbe is beépült [Artificial Intelligence has also been Integrated into the Hungarian Military]. Magyar Nemzet, 9 September 2025. Online: https://magyarnemzet.hu/belfold/2025/09/a-mesterseges-intelligencia-a-magyar-honvedsegbe-is-beepult European Commission (2018): EU Member States Sign Up to Cooperate on Artificial Intelligence. 10 April 2018. Online: https://digital-strategy.ec.europa.eu/en/news/eu-member-states-sign-cooperate-artificial-intelligence European Commission (2023): Commission Recommendation of 03 October 2023 on Critical Technology Areas for the EU’s Economic Security for Further Risk Assessment with Member States. C(2023) 6689 final. Online: https://defence-industry-space.ec.europa.eu/commission-recommendation-03-october-2023-critical-technology-areas-eus-economic-security-further_en European Commission (2025): State of the Union Address by President von der Leyen, 10 September 2025. Online: https://ec.europa.eu/commission/presscorner/detail/en/SPEECH_25_2053 European Council – NATO (2025): Tenth Progress Report on the Implementation of the Common Set of Proposals Endorsed by EU and NATO Councils on 6 December 2016 and 5 December 2017. Online: https://www.consilium.europa.eu/media/f54kvokr/250605-progress-report-nr10-eu-nato.pdf European Parliament (2018): European Parliament Resolution of 12 September 2018 on Autonomous Weapon Systems (2018/2752(RSP). Online: https://www.europarl.europa.eu/doceo/document/TA-8-2018-0341_EN.html European Parliament (2025): White paper on the future of European Defence. European Parliament Resolution of 12 March 2025 on the White Paper on the Future of European Defence (2025/2565(RSP)). Online: https://www.europarl.europa.eu/doceo/document/TA-10-2025-0034_EN.pdf FRATSYVIR, Anna (2025): NATO Expands Satellite Surveillance to Monitor Ukraine, Eastern Flank. 12 June 2025. Online: https://kyivindependent.com/nato-expands-satellite-surveillance-to-monitor-ukraine-eastern-flank/ Honvéd Vezérkar [Armed Forces General Staff] (2025): Újdonságok a védelmi innováció területén [News in the Field of Defence Innovation]. Honvédelem.hu, 2 June 2025. Online: https://honvedelem.hu/hirek/ujdonsagok-a-vedelmi-innovacio-teruleten.html ISO/IEC (2023a): Information Technology – Artificial Intelligence – AI System Life Cycle Processes. ISO/IEC 5338. First edition. ISO/IEC (2023b): Information Technology – Artificial intelligence – Guidance on Risk Management. ISO/IEC 23894. First edition. JENKINS, Michael P. (2023): The Impact and Associated Risks of AI on Future Military Operations. Federal News Network, 18 October 2023. Online: https://federalnewsnetwork.com/commentary/2023/10/the-impact-and-associated-risks-of-ai-on-future-military-operations/ KAJAL, Kapil (2025): France Plans to Deploy Combat Robots by 2027, Eyes Full Robot Army by 2040. Interesting Engineering, 8 May 2025. Online: https://interestingengineering.com/military/france-eyes-all-robot-army-by-2040 KOLLÁR, Csaba (2019): A mesterséges intelligencia, mint komplex rendszer információbiztonsági kihívásai [Information Security Challenges of Artificial Intelligence as a Complex System]. In RAJNAI, Zoltán (ed.): Kiberbiztonság/Cybersecurity. Budapest: Biztonságtudományi Doktori Iskola, 62–70. Online: https://drkollar.hu/wp-content/uploads/2020/01/kiadvany-2019.pdf KOVÁCS, Zoltán ed. (2023): A mesterséges intelligencia és egyéb felforgató technológiák hatásainak átfogó vizsgálata [Comprehensive Review of the Impact of Artificial Intelligence and Other Disruptive Technologies]. Budapest: Katonai Nemzetbiztonsági Szolgálat. Magyarország Mesterséges Intelligencia Stratégiája (2025–2030) [Hungary’s Artificial Intelligence Strategy (2025–2030)]. (2025). Online: https://cdn.kormany.hu/uploads/document/c/c0/c0d/c0dfdbd37cfa520ae37361a168d244c85e7295af.pdf MALATRAS, Apostolos – DEDE, Georgia (2020): AI Cybersecurity Challenges. Threat Landscape for Artificial Intelligence. European Union Agency for Cybersecurity (ENISA). Online: https://doi.org/10.2824/238222 MYLREA, Michael – ROBINSON, Nikki (2023): Artificial Intelligence (AI) Trust Framework and Maturity Model: Applying an Entropy Lens to Improve Security, Privacy, and Ethical AI. Entropy, 25(10). Online: https://doi.org/10.3390/e25101429 NATO (2023): Speech by Secretary General Jens Stoltenberg at the NATO-Industry Forum. 25 Oct. 2023. Online: https://www.nato.int/cps/en/natohq/opinions_219128.htm NATO (2024): Summary of NATO’s revised Artificial Intelligence (AI) Strategy. 10 July 2024. Online: https://www.nato.int/cps/en/natohq/official_texts _227237.htm NATO (2025a): NATO Acquires AI-Enabled Warfighting System. 14 April 2025. Online: https://shape.nato.int/news-releases/nato-acquires-aienabled-warfighting-system- NATO (2025b): NATO Science & Technology Strategy. Defending the future, today! Online: https://www.nato.int/content/dam/nato/webready/documents/sto/STO-strategy-2025.pdf NÉGYESI, Imre (2022): A mesterséges intelligencia katonai felhasználásának lehetőségei: Első kötet [The Possibilities of Military Use of Artificial Intelligence Vol. I]. Budapest: HM Zrínyi Média Közhasznú Nonprofit Kft. NÉGYESI, Imre (2023): A mesterséges intelligencia katonai felhasználásának lehetőségei: II. kötet [The Possibilities of Military Use of Artificial Intelligence Vol. II]. Budapest: HM Zrínyi Média Közhasznú Nonprofit Kft. Nemzeti Kiberbiztonsági Intézet [National Cyber Security Centre] (2025): Elektronikus Információs Rendszerek és Szervezetek Kiberbiztonsági Követelménykatalógusának Alkalmazási Útmutatója. Kozkázatkezelés [Guideline for the Application of the Cybersecurity Requirements Catalogue of Electronic Information Systems and Organizations. Risk Management]. Online: https://nki.gov.hu/wp-content/uploads/2025/09/15.-Kockazatkezeles-ver.-1.1.pdf NIST (2023): Artificial Intelligence Risk Management Framework (AI RMF 1.0). Online: https://doi.org/10.6028/NIST.AI.100-1 PASCU, Corina – BARROS LOURENCO, Marco eds. (2023): Artificial Intelligence and Cybersecurity Research. ENISA Research and Innovation Brief. European Union Agency for Cybersecurity (ENISA). Online: https://data.europa.eu/doi/10.2824/808362 PUWAL, Steffan (2024): Should Artificial Intelligence Be Banned from Nuclear Weapons Systems? NATO Review, 12 April 2024. Online: https://archives.nato.int/uploads/r/nato-archives-online/d/3/8/d388000c2ba6b51ffb20865bb71d1828203cb6e45312f46ec1cf202fe918ca81/2024-04-12_Should_artificial_intelligence_be_banned_from_nuclear_weapons_systems_ENG.pdf SERALIDOU, Eleni – KIOSKLI, Kitty – FOTIS, Theofanis – POLEMI, Nineta (2025): AI_TAF: A Human-Centric Trustworthiness Risk Assessment Framework for AI Systems. Computers, 14(7). Online: https://doi.org/10.3390/computers14070243 TÖRÖK, Bernát – ZŐDI, Zsolt eds. (2021): A mesterséges intelligencia szabályozási kihívásai [The Regulatory Challenges of AI]. Budapest: Ludovika Egyetemi Kiadó. U.S. Department of State (2024): Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy. Online: https://www.state.gov/bureau-of-arms-control-deterrence-and-stability/political-declaration-on-responsible-military-use-of-artificial-intelligence-and-autonomy United Nations (2024): Lethal Autonomous Weapons Systems: Report of the Secretary-General (A/79/88). Online: https://digitallibrary.un.org/record/4059475 VOSS, Axel (2022): Report on Artificial Intelligence in a Digital Age. European Parliament Report A9 0088/2022. Online: https://www.europarl.europa.eu/doceo/document/A-9-2022-0088_EN.html Legal sources - 2021. évi CXL. törvény a honvédelemről és a Magyar Honvédségről [Act CXL of 2021 on National Defence and Hungarian Defence Forces] - 2024. évi LXIX. törvény Magyarország kiberbiztonságáról [Act LXIX of 2024 on the Cyber Security of Hungary] - 2025. évi ... törvény az Európai Unió mesterséges intelligenciáról szóló rendeletének magyarországi végrehajtásáról [Act of 2025 (draft) on the implementation of the European Union Regulation on Artificial Intelligence]. Online: https://cdn.kormany.hu/uploads/document/b/b9/b92/b929cdec547b87da4bd23bce694db86ce328e1c6.pdf - 1301/2024. (IX. 30.) Korm. határozat a mesterséges intelligenciáról szóló európai parlamenti és tanácsi rendelet végrehajtásához szükséges intézkedésekről [Government Resolution of 1301/2024 (IX. 30.) on measures necessary for the implementation of the Regulation of the European Parliament and of the Council on artificial intelligence] - 1149/2025. (V. 14.) Korm. határozat a mesterséges intelligenciáról szóló európai parlamenti és tanácsi rendelet végrehajtásához szükséges intézkedésekről szóló 1301/2024. (IX. 30.) Korm. határozatban foglalt feladatok végrehajtásáról [Government Resolution of 1149/2025 (V. 14.) on the implementation of the tasks set out in GR of 1301/2024 (IX. 30.) on the measures necessary for the implementation of the Regulation of the European Parliament and of the Council on artificial intelligence] - 7/2024. (VI. 24.) MK rendelet a biztonsági osztályba sorolás követelményeiről, valamint az egyes biztonsági osztályok esetében alkalmazandó konkrét védelmi intézkedésekről [Decree 7/2024 (VI. 24.) of the Prime Minister’s Office on the requirements for security classification and the specific protective measures applicable to each security classification] A Kormány rendelete az Európai Unió mesterséges intelligenciáról szóló rendeletének magyarországi végrehajtásáról szóló 2025. évi ... törvény végrehajtásáról [Decree of Government (draft) on the implementation of the on the implementation of the European Union Regulation on Artificial Intelligence] Commission Implementing Regulation (EU) 2024/2690 of 17 October 2024 laying down rules for the application of Directive (EU) 2022/2555 as regards technical and methodological requirements of cybersecurity risk-management measures and further specification of the cases in which an incident is considered to be significant with regard to DNS service providers, TLD name registries, cloud computing service providers, data centre service providers, content delivery network providers, managed service providers, managed security service providers, providers of online market places, of online search engines and of social networking services platforms, and trust service providers Council Decision of 23 September 2013 on the security rules for protecting EU classified information (2013/488/EU) Directive (EU) 2022/2555 of the European Parliament and of the Council of 14 December 2022 on measures for a high common level of cybersecurity across the Union, amending Regulation (EU) No 910/2014 and Directive (EU) 2018/1972, and repealing Directive (EU) 2016/1148 (NIS 2 Directive) Directive (EU) 2022/2557 of the European Parliament and of the Council of 14 December 2022 on the resilience of critical entities and repealing Council Directive 2008/114/EC Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) " ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(5) "17-38" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.2" ["abstract"]=> array(1) { ["en_US"]=> string(1192) "

The applicability of artificial intelligence (AI) is no longer in question today. AI has become so integrated into countless areas of the economy and society that we no longer even notice that a given service is provided by an AI system. The use of AI systems is a sensitive issue in many areas, such as the armed forces. This paper draws attention to the diversity of military applications of AI and the general risks involved. The armed forces need to use not just one or two AI systems, but dozens of AI solutions that must be integrated into military information systems. These AI systems perform tasks at different operational levels or support the operation of other systems.

The study provides guidance on the most important steps in the necessary risk management, based on the legal framework, standards and best practices. Tailored risk management provides the basis for local, system-specific regulation of military organisations, which must be compiled from existing cybersecurity framework elements. The study emphasises that AI systems cannot be exempt from cybersecurity regulations, so it is necessary to review and supplement existing tools and provide training.

" } ["subtitle"]=> array(1) { ["en_US"]=> string(49) "Military Requirements, Question Marks and Efforts" } ["title"]=> array(1) { ["en_US"]=> string(88) "Cybersecurity Challenges of the Integration of Artificial Intelligence (AI) Solutions" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(14) "Kassai Károly" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#774 (6) { ["_data"]=> array(15) { ["id"]=> int(11020) ["email"]=> string(23) "karoly.kassai@yahoo.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8580) ["seq"]=> int(2) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0009-0009-9398-6158" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(28) "a:1:{s:5:"hu_HU";s:3:"HVK";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(6) "Kassai" ["hu_HU"]=> string(6) "Kassai" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Károly" ["hu_HU"]=> string(7) "Károly" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(5) { [0]=> string(28) "artificial intelligence (AI)" [1]=> string(27) "military information system" [2]=> string(14) "cybersecurity," [3]=> string(15) "trustworthiness" [4]=> string(4) "risk" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#761 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46143) ["id"]=> int(6784) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8580) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#763 (6) { ["_data"]=> array(29) { ["id"]=> int(8695) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:39" ["primaryContactId"]=> int(11223) ["sectionId"]=> int(59) ["seq"]=> int(3) ["submissionId"]=> int(8570) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(4115) "ABBADI, Driss – LACHKAR, Abdelkader (2024): Cyber Threats in the Age of Artificial Intelligence. Exploiting Advanced Technologies and Strengthening Cybersecurity. International Journal of Science and Research Archive, 13(1), 2576–2588. Online: https://doi.org/10.30574/ijsra.2024.13.1.1961 AWS [s. a.]: What is Deep Learning in AI? AWS, s. a. Online: https://aws.amazon.com/what-is/deep-learning/ BADMAN, Annie – KOSINSKI, Matthew [s. a.]: What is AI security? IBM/think. Online: https://www.ibm.com/think/topics/ai-security ERDÉSZ, Viktor (2023): A mesterséges intelligencia alkalmazása a katonai nemzetbiztonsági hírszerzésben. Budapest: Katonai Nemzetbiztonsági Szolgálat. Online: https://bit.ly/4tKmx65 KIDD, Chrissy (2024): Cyber Kill Chains: Strategies & Tactics. Splunk, 26 August 2024. Online: https://www.splunk.com/en_us/blog/learn/cyber-kill-chains.html GOSS, Adam (2024): The Cyber Kill Chain: A Powerful Model For Analyzing Cyberattacks. Kraven Security, 11 March 2024. Online: https://kravensecurity.com/cyber-kill-chain/ Hungary’s Artificial Intelligence Strategy 2020–2030 (2020). AI Coalition – Digital Success Programme – Ministry for Innovation and Technology. Online: https://mik.neum.hu/wp-content/uploads/2025/03/2020-hungarian-AI-strategy.pdf JORDAN, Michael I. – MITCHELL, Tom M. (2015): Machine Learning: Trends, Perspectives, and Prospects. Science, 349(6245), 255–260. Online: https://doi.org/10.1126/science.aaa8415 Magyarország Mesterséges Intelligencia Stratégiája (2025–2030) [Hungary’s Artificial Intelligence Strategy (2025–2030)] (2025). Online: https://cdn.kormany.hu/uploads/document/c/c0/c0d/c0dfdbd37cfa520ae37361a168d244c85e7295af.pdf MATTIOLI, Rossella et al. (2023): Identifying Emerging Cybersecurity Threats and Challenges for 2030. European Union Agency for Cybersecurity (ENISA). Online: https://doi.org/10.2824/117542 MCCARTHY, John (2007): What is Artificial Intelligence? Online: https://www-formal.stanford.edu/jmc/whatisai.pdf Microsoft [s. a.a]: What is AI for Cybersecurity? Microsoft Security, s. a. Online: https://www.microsoft.com/en-us/security/business/security-101/what-is-ai-for-cybersecurity Microsoft [s. a.b]: What is the Cyber Kill Chain? Microsoft Security, s. a. Online: https://www.microsoft.com/en-us/security/business/security-101/what-is-cyber-kill-chain Pentera [s. a.]: Cyber Kill Chain. Glossary Pentera, s. a. Online: https://pentera.io/glossary/cyber-kill-chain-framework-explained/ RADLE, Andrew J. et al. (2023): MITRE FiGHTTM: High-Level Overview. MITRE Five-G Hierarchy of Threats (FiGHT). The MITRE Corporation. Online: https://fight.mitre.org/FiGHT_High-Level_Overview_PRS-23-2698.pdf Team Antier (2022): DAO és Metaverzum: Egy kiváló kombináció és megoldás a holnap világa számára. Antier Solutions. 4 October 2022. Online: https://www.antiersolutions.com/hu/blogok/A-dao-%C3%A9s-a-metaverzum-egy-kiv%C3%A1l%C3%B3-kombin%C3%A1ci%C3%B3-%C3%A9s-megold%C3%A1s-a-holnap-vil%C3%A1ga-sz%C3%A1m%C3%A1ra/ The Cyber Kill Chain® [s. a.]. Lockheed Martin. Online: https://www.lockheedmartin.com/en-us/capabilities/cyber/cyber-kill-chain.html USMAN, Yusuf – UPADHYAY, Aadesh – CHATAUT, Robin – GYAWALI, Prashnna K. (2024): Is Generative AI the Next Tactical Cyber Weapon for Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks. arXiv:2408.12806. Online: https://doi.org/10.48550/arXiv.2408.12806 VELAZQUEZ, Chris (2015): Detecting and Preventing Attacks Earlier in the Kill Chain. Global Information Assurance Certification Paper. The SANS Institute. Online: https://www.giac.org/paper/gsec/36774/detecting-preventing-attacks-earlier-kill-chain/145219 What Is a Cyber Kill Chain? | Pure Storage. (n.d.). Retrieved 28 October 2025, from Online: https://www.purestorage.com/knowledge/cyber-kill-chain.html WOLFENSTEIN, Konrad (2023): Mesterséges intelligencia vagy metaverzum? Mi a fontosabb? Vagy itt szinergiák alakulnak ki a további úttörő fejlesztések érdekében? Xpert.Digital, 28 December 2023. Online: https://xpert.digital/hu/ki-metaverzum/ " ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(5) "39-51" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.3" ["abstract"]=> array(1) { ["en_US"]=> string(854) "

The rapid advancement of artificial intelligence (AI) technology has fundamentally transformed the field of cybersecurity, impacting both defensive and offensive capabilities. This series of articles analyses the malicious applications of artificial intelligence in cyberattacks, structured around the Cyber Kill Chain model. It details how AI can increase the effectiveness, automation and stealth of attacks in all phases of cyberattacks described by the Cyber Kill Chain model, from reconnaissance to actions on objectives. The aim of this series of articles is to provide a comprehensive overview of current threats and to highlight the importance of further research and proactive defence strategies. This article, the first in the series, covers the definitions of AI and the Cyber Kill Chain model to the extent necessary for understanding.

" } ["subtitle"]=> array(1) { ["en_US"]=> string(78) "Fundamental Concepts of Artificial Intelligence and the Cyber Kill Chain Model" } ["title"]=> array(1) { ["en_US"]=> string(58) "The Use of Artificial Intelligence in Cyberattacks, Part 1" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(15) "Kovács Zoltán" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#775 (6) { ["_data"]=> array(15) { ["id"]=> int(11223) ["email"]=> string(20) "zkovacs.24@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8695) ["seq"]=> int(3) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0003-2174-8213" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(7) "Kovács" ["hu_HU"]=> string(7) "Kovács" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Zoltán" ["hu_HU"]=> string(7) "Zoltán" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(8) { [0]=> string(23) "artificial intelligence" [1]=> string(13) "cybersecurity" [2]=> string(11) "cyberattack" [3]=> string(16) "Cyber Kill Chain" [4]=> string(16) "machine learning" [5]=> string(13) "deep learning" [6]=> string(8) "phishing" [7]=> string(7) "malware" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#784 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46144) ["id"]=> int(6785) ["isApproved"]=> bool(false) ["locale"]=> string(5) "en_US" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8695) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#773 (6) { ["_data"]=> array(29) { ["id"]=> int(8696) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:39" ["primaryContactId"]=> int(11224) ["sectionId"]=> int(59) ["seq"]=> int(4) ["submissionId"]=> int(8571) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(8761) "ABBADI, Driss – LACHKAR, Abdelkader (2024): Cyber Threats in the Age of Artificial Intelligence. Exploiting Advanced Technologies and Strengthening Cybersecurity. International Journal of Science and Research Archive, 13(1), 2576–2588. Online: https://doi.org/10.30574/ijsra.2024.13.1.1961 AL-AZZAWI, Mays – DOAN, Dung – SIPOLA, Tuomo – HAUTAMÄKI, Jari – KOKKONEN, Tero (2025): Red Teaming with Artificial Intelligence-Driven Cyberattacks: A Scoping Review. arXiv:2503.19626. Online: https://doi.org/10.48550/arXiv.2503.19626 ALRZINI, Joma – PENNINGTON, Diane (2020): A Review of Polymorphic Malware Detection Techniques. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(12), 1238–1247. Online: https://doi.org/10.34218/IJARET.11.12.2020.119 AMSTER, Alex [s. a.]: Automating Vulnerability Detection in Networks with AI. AllStarsIT, s. a. Online: https://www.allstarsit.com/blog/automating-vulnerability-detection-in-networks-with-ai ARIF, Aftab – KHAN, Muhammad Ismaeel – KHAN, Ali Raza A (2024): An Overview of Cyber Threats Generated by AI. International Journal of Multidisciplinary Sciences and Arts, 3(4), 67–76. Online: https://doi.org/10.47709/ijmdsa.v3i4.4753 BLAKE, Harrison (2025): AI-Powered Threats in Supply Chains: A Looming Cybersecurity Challenge. ResearchGate. Online: https://www.researchgate.net/profile/Harrison-Blake-2/publication/389274676_AI-Powered_Threats_in_Supply_Chains_A_Looming_Cybersecurity_Challenge/links/67bc8c29461fb56424e8923e/AI-Powered-Threats-in-Supply-Chains-A-Looming-Cybersecurity-Challenge.pdf Cybersecurity Forecast 2025 (2025): Google Cloud Security. Online: https://cloud.google.com/blog/topics/threat-intelligence/cybersecurity-forecast-2025 DEAN, B. (2025): New Report: Over 80% of Cyberattacks Now Use AI. Programs.com, 8 August 2025. Online: https://programs.com/resources/ai-cyberattack-stats/ DEES, Mels (2025): CrowdStrike Introduces Tools to Block Malicious AI Models. Techzine Global, 30 April 2025. Online: https://www.techzine.eu/news/security/130990/crowdstrike-introduces-tools-to-block-malicious-ai-models/ FADHIL, Ammar (2025): Enhancing Data Security: A Hybrid Approach of AI-Driven Steganography and Encryption. The Indonesian Journal of Computer Science, 14(2). Online: https://doi.org/10.33022/ijcs.v14i2.4759 FALADE, Polra V. (2023): Decoding the Threat Landscape: ChatGPT, FraudGPT, and WormGPT in Social Engineering Attacks. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(5), 185–198. Online: https://doi.org/10.32628/CSEIT2390533 FERNÁNDEZ, Rodrigo (2025): AI-Driven Supply Chain Attacks: The New Cyber Risk in 2025. NeuralTrust, 25 September 2025. Online: https://neuraltrust.ai/blog/ai-driven-supply-chain-attacks FRITSCH, Lothar – JABER, Aws – YAZIDI, Anis (2022): An Overview of Artificial Intelligence Used in Malware. In ZOUGANELI, Evi – YAZIDI, Anis – MELLO, Gustavo – LIND, Pedro (eds.): Nordic Artificial Intelligence Research and Development. Cham: Springer International Publishing, 41–51. Online: https://doi.org/10.1007/978-3-031-17030-0_4 GILES, Lionel (2013): Sun Tzu on the Art of War. London: Routledge. Online: https://doi.org/10.4324/9781315030081 GLYNN, Fergal (2025): AI Vulnerability Scanner: 6 Practical Metrics Every Security Team Should Monitor. Mindgard, 25 August 2025. Online: https://mindgard.ai/blog/ai-vulnerability-scanner-metrics GOODFELLOW, Ian et al. (2020): Generative Adversarial Networks. Communications of the ACM, 63(11), 139–144. Online: https://doi.org/10.1145/3422622 HAUROGNÉ, Jean – BASHEER, Nihala – ISLAM, Shareeful (2024): Vulnerability Detection Using BERT based LLM Model with Transparency Obligation Practice towards Trustworthy AI. Machine Learning with Applications, 18. Online: https://doi.org/10.1016/j.mlwa.2024.100598 HITAJ, Briland – GASTI, Paolo – ATENIESE, Giuseppe – PEREZ-CRUZ, Fernando (2019): PassGAN: A Deep Learning Approach for Password Guessing. arXiv:1709.00440. Online: https://doi.org/10.48550/arXiv.1709.00440 HUTCHINS, Eric M. – CLOPPERT, Michael J., – AMIN, Rohan M. (2011): Intelligence-Driven Computer Network Defense Informed by Analysis of Adversary Campaigns and Intrusion Kill Chains. Leading Issues in Information Warfare & Security Research, 1(1), 1–14. itszótár.hu (2025): Metamorf és polimorf kártevők: Ezen kártékony szoftverek működésének magyarázata. ITszotar.hu, 15 May 2025. Online: https://itszotar.hu/metamorf-es-polimorf-kartevok-ezen-kartekony-szoftverek-mukodesenek-magyarazata/ KUMAR, Ankit – CHAUHAN, Nidhi (2025): AI-Driven Optimization for Enhancing Performance, Efficiency, and Personalization in Content Delivery Networks. International Journal of Computer Techniques, 12(3), 1–9. Online: https://ijctjournal.org/wp-content/uploads/2025/06/AI-Driven-Optimization-for-Enhancing-Performance-Efficiency-and-Personalization-in-Content-Delivery-Networks.pdf LUONG, Phung D. et al. (2025): xOffense: An AI-driven Autonomous Penetration Testing Framework with Offensive Knowledge-Enhanced LLMs and Multi Agent Systems. arXiv:2509.13021v1. Online: https://arxiv.org/html/2509.13021v1 Microsoft [s. a.]: What is the Cyber Kill Chain? Microsoft Security, s. a. Online: https://www.microsoft.com/en-us/security/business/security-101/what-is-cyber-kill-chain MIRSKY, Yisroel et al. (2023): The Threat of Offensive AI to Organizations. Computers & Security, 124. Online: https://doi.org/10.1016/j.cose.2022.103006 Navigating a New Threat Landscape (2024). Darktrace. Online: https://www.darktrace.com/resources/navigating-a-new-threat-landscape NOBLES, Calvin (2024): The Weaponization of Artificial Intelligence in Cybersecurity: A Systematic Review. Procedia Computer Science, 239, 547–555. Online: https://doi.org/10.1016/j.procs.2024.06.206 PARK, Jin H. – AYATI, Seyyed A. – CAI, Yichen (2025): Improving Acoustic Side-Channel Attacks on Keyboards Using Transformers and Large Language Models. arXiv:2502.09782. Online: https://doi.org/10.48550/arXiv.2502.09782 Phishing Trends Report (Updated for 2025) [s. a.]. Hoxhunt, s. a. Online: https://hoxhunt.com/guide/phishing-trends-report POTTER, Yujin et al. (2025): Frontier AI’s Impact on the Cybersecurity Landscape. arXiv:2504.05408. Online: https://doi.org/10.48550/arXiv.2504.05408 ROHLF, Chris (2025): AI and the Software Vulnerability Lifecycle. Center for Security and Emerging Technology, 8 August 2025. Online: https://cset.georgetown.edu/article/ai-and-the-software-vulnerability-lifecycle/ SALEM, Maher – MRIAN, Mohammad (2025): AI-Driven Penetration Testing: Automating Exploits with LLMs and Metasploit-A VSFTPD Case Study. 2025 International Conference on New Trends in Computing Sciences (ICTCS), Amman, Jordan, 89–96. Online: https://doi.org/10.1109/ICTCS65341.2025.10989363 SCHRÖER, Saskia L. – PAJOLA, Luca – CASTAGNARO, Alberto – APRUZZESE, Giovanni – CONTI, Mauro (2025): Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI. arXiv:2506.12519v2. Online: https://arxiv.org/html/2506.12519 SentinelOne (2025): What is Polymorphic Malware? Examples & Challenges. SentinelOne, 20 August 2025. Online: https://www.sentinelone.com/cybersecurity-101/threat-intelligence/what-is-polymorphic-malware/ SINGH, Bhagwant – CHEEMA, Sikander S. (2024): Emerging Trends in AI-Powered Malware Detection: A Review of Real-Time and Adversarially Resilient Techniques. Tuijin Jishu/Journal of Propulsion Technology, 45(4). SYED, Shoeb A. (2025): Adversarial AI and Cybersecurity: Defending Against AI- Powered Cyber Threats. Iconic Research and Engineering Journals, 8(9), 1030–1041. USMAN, Yusuf – UPADHYAY, Aadesh – CHATAUT, Robin – GYAWALI, Prashnna K. (2024): Is Generative AI the Next Tactical Cyber Weapon for Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks. arXiv:2408.12806. Online: https://doi.org/10.48550/arXiv.2408.12806 VERTON, Dan (2025): The 2025 Cybersecurity Pulse Report. iSMG, 30 May 2025. Online: https://ismg.io/resource/rsac-2025-pulse/ YAMIN, Muhammad M. – ULLAH, Mohib – ULLAH, Habib – KATT, Basel (2021): Weaponized AI for Cyber Attacks. Journal of Information Security and Applications, 57. Online: https://doi.org/10.1016/j.jisa.2020.102722 YU, Jingru et al. (2024): The Shadow of Fraud: The Emerging Danger of AI-powered Social Engineering and its Possible Cure (Version 1). arXiv:2407.15912. Online: https://doi.org/10.48550/ARXIV.2407.15912 ZHU, Yuxuan et al. (2025): CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities. arXiv:2503.17332v4. Online: https://arxiv.org/html/2503.17332v4" ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(5) "53-67" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.4" ["abstract"]=> array(1) { ["en_US"]=> string(871) "

The first part of this series of articles provided an overview of artificial intelligence (AI) and its various subfields (e.g. machine learning, generative AI, etc.), and showed that the Cyber Kill Chain (CKC) model, despite all its limitations, is suitable for achieving the goal of this series of articles, i.e. it can be used to demonstrate how attackers can use AI in cyberattacks. In order to develop adequate cyber defence against AI-assisted cyberattacks, it is necessary to know what AI-assisted tools attackers can use in each phase of the attack. This article focuses on the first four phases of the CKC model (reconnaissance, weaponization, delivery and exploitation) to examine where and how attackers are already using artificial intelligence in the first four phases of the Cyber Kill Chain model to achieve their goals, and how this helps attackers.

" } ["subtitle"]=> array(1) { ["en_US"]=> string(43) "Phases 1– 4 of the Cyber Kill Chain Model" } ["title"]=> array(1) { ["en_US"]=> string(58) "The Use of Artificial Intelligence in Cyberattacks, Part 2" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(15) "Kovács Zoltán" } ["locale"]=> string(5) "en_US" ["authors"]=> array(1) { [0]=> object(Author)#785 (6) { ["_data"]=> array(15) { ["id"]=> int(11224) ["email"]=> string(20) "zkovacs.24@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8696) ["seq"]=> int(4) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0003-2174-8213" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(7) "Kovács" ["hu_HU"]=> string(7) "Kovács" } ["givenName"]=> array(2) { ["en_US"]=> string(7) "Zoltán" ["hu_HU"]=> string(7) "Zoltán" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "en_US" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(1) { ["en_US"]=> array(9) { [0]=> string(23) "artificial intelligence" [1]=> string(13) "cybersecurity" [2]=> string(11) "cyberattack" [3]=> string(16) "Cyber Kill Chain" [4]=> string(5) "OSINT" [5]=> string(7) "exploit" [6]=> string(7) "evasion" [7]=> string(8) "phishing" [8]=> string(7) "malware" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#790 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46145) ["id"]=> int(6786) ["isApproved"]=> bool(false) ["locale"]=> string(5) "hu_HU" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8696) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#770 (6) { ["_data"]=> array(28) { ["id"]=> int(8788) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:39" ["primaryContactId"]=> int(11355) ["sectionId"]=> int(59) ["seq"]=> int(5) ["submissionId"]=> int(8663) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(5052) "- 2024. évi LXIX. törvény Magyarország kiberbiztonságáról. Online: https://net.jogtar.hu/jogszabaly?docid=a2400069.tv BÁNYÁSZ Péter – ORBÓK Ákos (2013): A NATO kibervédelmi politikája és kritikus infrastruktúra védelme a közösségi média tükrében. Hadtudomány, 23(E-szám), 188–209. Online: https://ojs.mtak.hu/index.php/hadtudomany/article/view/6705/5304 CLARKE, Richard A. – KNAKE, Robert K. (2019): The Fifth Domain. [H. n.]: Penguin Books. Online: https://www.penguinrandomhouse.com/books/600219/the-fifth-domain-by-richard-a-clarke-and-robert-k-knake/ ENISA (2024): Cyber Europe 2024: Unveiling Key Insights From the Cyber Exercise That Tested the Cybersecurity of EU’s Energy Sector. Online: https://www.enisa.europa.eu/news/cyber-europe-2024-unveiling-key-insights-from-the-cyber-exercise-that-tested-the-cybersecurity-of-eus-energy-sector ERTAN, A. et al. szerk. (2020): Cyber Threats and NATO 2030: Horizon Scanning and Analysis. Tallinn: CCD COE. Online: https://ccdcoe.org/uploads/2020/12/Cyber-Threats-and-NATO-2030_Horizon-Scanning-and-Analysis.pdf HAIG Zsolt (2023): A kibertéri műveletek fejlődése: a számítógép-hálózati műveletektől a kibertéri befolyásolásig. In KRASZNAY Csaba (szerk.): Taktikák és stratégiák a kiberhadviselésben. Budapest: Ludovika. Online: https://tudasportal.uni-nke.hu/xmlui/handle/20.500.12944/102124?key=Kiberv%C3%A9delem%20%C3%A9s%20nemzetbiztons%C3%A1g%20kiss KERTÉSZ Bence (2023): Kiberműveletek az Izrael és Hamász közötti háborúban. biztonságpolitika.hu, 2023. október 30. Online: https://biztonsagpolitika.hu/cikksorozatok/kibermuveletek-az-izrael-es-hamasz-kozotti-haboruban KISS Álmos Péter (2019): A hibrid hadviselés természetrajza. Honvédségi Szemle, 147(4), 17–37. Online: https://real.mtak.hu/125219/1/HSZ_2019_147_4_Kiss_Almos_Peter.pdf KOVÁCS László (2021): Offenzív kiberműveletek II.: Kibererők és képességeik. Hadmérnök, 16(3), 119–137. Online: https://doi.org/10.32567/hm.2021.3.7 KOVÁCS László (2023): Hadviselés a 21. században: kiberműveletek. Budapest: Ludovika. LIBICKI, Martin (2016): Cyberspace in Peace and War. [H. n.]: Naval Institute Press. Online: https://books.google.hu/books/about/Cyberspace_in_Peace_and_War.html?id=m4f9DAAAQBAJ&redir_esc=y MENCZELESZ Adrián (2025): Digitális védelem a 21. században – hazánk kiberbiztonsági stratégiája és annak megvalósítása. Jogászvilág, 2025. június 5. Online: https://jogaszvilag.hu/napi/digitalis-vedelem-a-21-szazadban-hazank-kiberbiztonsagi-strategiaja-es-annak-megvalositasa/# NATO (2016): Warsaw Summit Communiqué. Online: https://www.nato.int/cps/en/natohq/official_texts_133169.htm NATO (2025): NATO Cyber Coalition 2025. Advancing Cyber Defence and Strengthening Alliance Resilience. Online: https://www.act.nato.int/article/cyber-coalition-2025/ NATO Standard Allied Joint Publication-3.20. Allied Joint Doctrine for Cyberspace Operations (2020). Online: https://assets.publishing.service.gov.uk/media/5f086ec4d3bf7f2bef137675/doctrine_nato_cyberspace_operations_ajp_3_20_1_.pdf NATO CCDCOE (2022): NATO Cyberspace Exercises: Moving Ahead CyCon 2022 Workshop Summary. Online: https://ccdcoe.org/library/publications/nato-cyberspace-exercises-moving-ahead-cycon-2022-workshop-summary/ NATO Cyber Defence Pledge (2016). Online: https://www.nato.int/en/about-us/official-texts-and-resources/official-texts/2016/07/08/cyber-defence-pledge RESPERGER István (2018): A válságkezelés és a hibrid hadviselés. Budapest: Dialóg Campus. Online: https://bit.ly/4sFA7Gx RID, Thomas (2020): Active Measures. The Secret History of Disinformation and Political Warfare. [H. n.]: Profile Books. Online: https://books.google.hu/books/about/Active_Measures.html?id=lWltDwAAQBAJ&redir_esc=y SCHMITT, Michael N. szerk. (2017): Tallinn Manual 2.0 on the International Law Applicable to Cyber Operations. Cambridge: Cambridge University Press. Online: https://assets.cambridge.org/97811071/77222/frontmatter/9781107177222_frontmatter.pdf ŞEKER, Ensar (2019): The Concept of Cyber Defence Exercises (CDX): Planning, Execution, Evaluation. arXiv:1906.03184. Online: https://doi.org/10.1109/CyberSecPODS.2018.8560673 SZABÓ András (2018): Ajánlás TTX gyakorlatok szervezéséhez. Hadmérnök, 13(KÖFOP), 235–251. Online: http://www.hadmernok.hu/180kofop_14_szabo.pdf Szöllősi Gergely (2024): Locked Shields 2024: Kimagasló magyar eredmény. Honvédelem, 2024. május 21. Online: https://honvedelem.hu/hirek/locked-shields-2024-kimagaslo-magyar-eredmeny.html VYKOPAL, Jan et al. (2017): Timely Feedback in Unstructured Cybersecurity Exercises. arXiv:1712.09424. Online: https://doi.org/10.48550/arXiv.1712.09424 ZACHARIS, Alexandros – KATOS, Vasilios – PATSAKIS, Constantinos (2024): Integrating AI-Driven Threat Intelligence and Forecasting in the Cyber Security Exercise Content Generation Lifecycle. International Journal of Information Security, 23(4), 1–20. Online: https://doi.org/10.1007/s10207-024-00860-w" ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(5) "69-85" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.5" ["abstract"]=> array(2) { ["en_US"]=> string(895) "

Cyberspace has become one of the most significant operational domains in modern security and military strategy over the past decade. Consequently, the development of cyber defence and cyber operations capabilities has gained strategic importance at both national and international levels. Cyber defence and cyber operations exercises play a crucial role in preparedness, as they aim to enhance technical, organizational and leadership capabilities in realistic scenarios. This paper examines the transformation of the cyber defence and cyber operations exercise system, analyzes the driving factors behind this evolution, and explores current challenges such as rapid technological development and international cooperation. The study highlights that future cyber exercises will increasingly rely on complex, adaptive and multidisciplinary approaches to ensure effective cyber resilience.

" ["hu_HU"]=> string(979) "

A kibertér az elmúlt évtizedben a biztonságpolitika és a katonai műveletek egyik meghatározó dimenziójává vált. A kibervédelmi és kiberműveleti képességek fejlesztése ennek megfelelően stratégiai jelentőségű feladat mind nemzeti, mind nemzetközi szinten. A felkészülés egyik legfontosabb eszközét a kibervédelmi és kiberműveleti gyakorlatok jelentik, amelyek célja a technikai, szervezeti és vezetői képességek fejlesztése valósághű környezetben. A tanulmány bemutatja e gyakorlatok rendszerének átalakulását, elemzi a fejlődést kiváltó tényezőket, valamint vizsgálja azokat az aktuális kihívásokat – különösen a technológiai fejlődés és a nemzetközi együttműködés területén –, amelyek meghatározzák a gyakorlatok hatékonyságát. Az elemzés rámutat arra, hogy a jövőben a komplex, adaptív és multidiszciplináris megközelítés válik meghatározóvá a kibervédelmi felkészítésben.

" } ["title"]=> array(2) { ["en_US"]=> string(108) "The Transformation of Cybersecurity and Cyber Operations Exercises and the Examination of Current Challenges" ["hu_HU"]=> string(114) "A kibervédelmi és a kiberműveleti gyakorlatok rendszerének átalakulása, az aktuális kihívások vizsgálata" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(15) "Pozderka Gábor" } ["locale"]=> string(5) "hu_HU" ["authors"]=> array(1) { [0]=> object(Author)#786 (6) { ["_data"]=> array(15) { ["id"]=> int(11355) ["email"]=> string(15) "poz@freemail.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8788) ["seq"]=> int(5) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(0) "" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(12) "+36302987620" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(8) "Pozderka" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(6) "Gábor" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(5) { [0]=> string(13) "kibervédelem" [1]=> string(15) "kiberműveletek" [2]=> string(16) "kibergyakorlatok" [3]=> string(4) "NATO" [4]=> string(12) "átalakulás" } ["en_US"]=> array(5) { [0]=> string(13) "cyber defence" [1]=> string(16) "cyber operations" [2]=> string(15) "cyber exercises" [3]=> string(4) "NATO" [4]=> string(14) "transformation" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#789 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46146) ["id"]=> int(6787) ["isApproved"]=> bool(false) ["locale"]=> string(5) "hu_HU" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8788) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#797 (6) { ["_data"]=> array(29) { ["id"]=> int(8515) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:40" ["primaryContactId"]=> int(10886) ["sectionId"]=> int(59) ["seq"]=> int(6) ["submissionId"]=> int(8390) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(2223) "ALEXANDER, Otis – BELISLE, Misha – STEELE, Jacob (2020): MITRE ATT&CK® for In-dustrial Control Systems: Design and Philosophy. Bedford, MA, USA: The MITRE Corpo-ration, 21–85. ANDERSSON, Niklas (2023): The Effect of the IT/OT Gap on the NIS 2 Implementation. Szakdolgozat. Stockholm: Stockholm University Department of Computer and Systems Sci-ences. Online: https://su.diva-portal.org/smash/record.jsf?pid=diva2%3A1784461&dswid=5127 CrowdStrike 2026. Global Threat Report. Online: https://www.crowdstrike.com/en-us/global-threat-report/ Dragos (2025): 2025 OT. Cybersecurity Action Guide. Online: https://hub.dragos.com/hubfs/312-Year-in-Review/2025/Dragos_2025_OT_Cybersecurity_Global_Action_Guide.pdf?hsLang=en Fortinet (2025): ZTNA vs VPN – What's The Better Cybersecurity Solution? Online: https://www.fortinet.com/resources/cyberglossary/ztna-vs-vpn FRÉSZ Ferenc (2025): Milliárdnyi kiszivárgott hitelesítő adat. Online: https://substack.com/@ferencfresz/p-166319450 KOCSIS Tamás (2025): Ipari (OT) kiberbiztonsági szakember képzés. Óbudai Egyetem Neumann János Informatikai Kar, prezentáció. LEE, Robert M. – CONWAY, Tim (2022): The Five ICS Cybersecurity Critical Controls. SANS. Online: https://sansorg.egnyte.com/dl/R0r9qGEhEe LOBO, Ruben (2023): Zero Trust Network Access (ZTNA) – Revolutionizing Remote Ac-cess Security Across OT Environments. Industrial Cyber, 2023. december 3. Online: https://industrialcyber.co/zero-trust/zero-trust-network-access-ztna-revolutionizing-remote-access-security-across-ot-environments/ MAVROUDIS, Vasilios (2024): Zero-Trust Network Access (ZTNA). Online: https://doi.org/10.48550/arXiv.2410.20611 MITRE Corp. (2025): ICS Matrix. Online: https://attack.mitre.org/matrices/ics/ SCOTT, Rose et al. (2020): Zero Trust Architecture. NIST Special Publication 800-207. On-line: https://doi.org/10.6028/NIST.SP.800-207 The Claroty Team (2023): ICS Security: The Purdue Model. Online: https://claroty.com/blog/ics-security-the-purdue-model ZAYTSEV, Alexey (2023): OT Remote Access: Can You Trust Your Technician’s Laptop? Cisco Blogs, 2023. november 9. Online: https://blogs.cisco.com/industrial-iot/ot-remote-access-can-you-trust-your-technicians-laptop" ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(6) "87-102" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.6" ["abstract"]=> array(2) { ["en_US"]=> string(926) "

The thesis examines the cybersecurity challenges of remote access to industrial systems (OT), with particular emphasis on the vulnerabilities of VPN-based solutions and the potential implementation of zero trust network access (ZTNA) technology built on zero trust architecture. It compares the operation of VPN and ZTNA, highlighting advantages and limitations such as the extent to which the principle of least privilege is enforced, identity-based access management, and simplified policy control. The study also explores the possibilities for integrating ZTNA into OT environments, taking into account the Purdue model and addressing architectural feasibility as well as other implementation challenges. The findings indicate that while ZTNA can enhance OT cybersecurity, its successful deployment depends on proper infrastructure preparation, gradual rollout, and careful consideration of the cost–benefit ratio.

" ["hu_HU"]=> string(1062) "

A cikk az ipari rendszerek (OT) távelérésének kiberbiztonsági kihívásait vizsgálja, különös tekintettel a VPN-alapú megoldások sérülékenységeire és a zero trust architektúrára épülő zero trust network access (ZTNA) technológia bevezetésének lehetőségeire. A cikk összehasonlítja a VPN és a ZTNA működését, bemutatva az előnyöket és korlátokat, mint például a legkisebb jogosultság elvének való megfelelés mértéke, az identitásalapú hozzáféréskezelés és az egyszerűbb szabálykezelés. Emellett vizsgálja a ZTNA OT-környezetbe történő integrációs lehetőségeit a Purdue-modellt is figyelembevéve, kitérve az architekturális megvalósíthatósági lehetőségekre, felhívva a figyelmet az implementáció egyéb kihívásaira. A szakirodalmi áttekintés alapján megállapítható, hogy a ZTNA növelheti az OT kiberbiztonságát, ugyanakkor sikeres bevezetése a megfelelő infrastruktúra-előkészítésen, fokozatos bevezetésen és a költség-haszon arány mérlegelésén is múlik.

" } ["subtitle"]=> array(2) { ["en_US"]=> string(58) "New Approaches of Remote Access in Industrial Environments" ["hu_HU"]=> string(55) "Ipari rendszerek távelérésének új megközelítése" } ["title"]=> array(2) { ["en_US"]=> string(74) "Zero Trust Network Access Solutions in Operational Technology Environments" ["hu_HU"]=> string(58) "Zero trust network access az ipari (OT) kiberbiztonságban" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(12) "Tóth Ádám" } ["locale"]=> string(5) "hu_HU" ["authors"]=> array(1) { [0]=> object(Author)#798 (6) { ["_data"]=> array(11) { ["id"]=> int(10886) ["email"]=> string(19) "tothad.20@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8515) ["seq"]=> int(6) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["affiliation"]=> array(1) { ["hu_HU"]=> string(41) "a:1:{s:5:"hu_HU";s:15:"Óbudai Egyetem";}" } ["familyName"]=> array(1) { ["hu_HU"]=> string(5) "Tóth" } ["givenName"]=> array(1) { ["hu_HU"]=> string(6) "Ádám" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(6) { [0]=> string(16) "ipari rendszerek" [1]=> string(2) "OT" [2]=> string(16) "távoli elérés" [3]=> string(4) "ZTNA" [4]=> string(10) "zero trust" [5]=> string(25) "zero trust network access" } ["en_US"]=> array(5) { [0]=> string(25) "industrial control system" [1]=> string(2) "OT" [2]=> string(13) "remote access" [3]=> string(4) "ZTNA" [4]=> string(25) "zero trust network access" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#799 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46147) ["id"]=> int(6788) ["isApproved"]=> bool(false) ["locale"]=> string(5) "hu_HU" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8515) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#716 (6) { ["_data"]=> array(29) { ["id"]=> int(8545) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:40" ["primaryContactId"]=> int(10949) ["sectionId"]=> int(56) ["seq"]=> int(1) ["submissionId"]=> int(8420) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(2947) "Apophis ESA (2025). Online: https://www.esa.int/Space_Safety/Planetary_Defence/Apophis ARTEMIEVA, Natalia – SHUVALOV, Valery (2016): From Tunguska to Chelyabinsk via Jupiter. Annual Review Earth & Planetary Sciences, 44, 37–56. Online: https://doi.org/10.1146/annurev-earth-060115-012218 BINZEL, Richard P. (2000): The Torino Impact Hazard Scale. Planetary and Space Science, 48(4), 297–303. Online: https://doi.org/10.1016/S0032-0633(00)00006-4 BOROVIČKA, Jiří (2016): The Chelyabinsk Event. Proceedings of the International Astronomical Union, 11(A29A), 247–252. Online: https://doi.org/10.1017/S1743921316002982 CHESLEY, Steven. R. et al. (2002): Quantifying the Risk Posed by Potential Earth Impacts. Icarus, 159(2), 423–432. Online: https://doi.org/10.1006/icar.2002.6910 ÉRCES Gergő – VASS Gyula (2018): Veszélyes ipari üzemek tűzvédelme – ipari üzemek fenntartható tűzbiztonságának fejlesztési lehetőségei a komplex tűzvédelem tekintetében. Műszaki Katonai Közlöny, 28(4), 2–22. Online: https://mkk.uni-nke.hu/document/mkk-uni-nke-hu/2018_4_01_Erces%20G%20-%20Vass%20Gy_MKK_cikk.pdf KERESZTÚRI Ákos – SÁRNECZKY Krisztián (2023): Célpont a Föld? Kisbolygók a láthatáron. Budapest: Magyar Csillagászati Egyesület. KERESZTÚRI Ákos – TÓTH Imre (2008): Kisbolygó vagy üstökös? A Tunguz-esemény. Meteor, 38(6), 3–9. Online: https://epa.oszk.hu/03000/03054/00358/pdf/EPA03054_meteor_2008_06.pdf KRING, David A. (2017): Guidebook to the Geology of Barringer Meteorite Crater, Arizona (a.k.a. Meteor Crater). [H. n.]: Lunar and Planetary Institute. MORRISON, David (2018): Tunguska Workshop: Applying Modern Tools to Understand the 1908 Tunguska Impact. NASA/Technical Memorandum (NASA/TM—220174). Online: https://ntrs.nasa.gov/api/citations/20190002302/downloads/20190002302.pdf PALLAGI András (2023): Kritikus infrastuktúrák védelmének vizsgálata. PhD-disszertáció. Budapest: Óbudai Egyetem Biztonságtudományi Doktori Iskola. Online: https://bdi.uni-obuda.hu/wp-content/uploads/2024/06/Doktori-PhD-ertekezes-Pallagi-Andras.pdf POPE, Kevin O. et al. (1997): Energy, Volatile Production, and Climatic Effects of the Chicxulub Cretaceous/Tertiary Impact. Journal of Geophysical Research, 102(E9), 21645–21664. Online: https://doi.org/10.1029/97JE01743 REDDY, Vishnu et al. (2022): Apophis Planetary Defense Campaign. The Planetary Science Journal, 3(5), 1–16. Online: https://doi.org/10.3847/PSJ/ac66eb SCHMIEDER, Martin – KRING, David A. (2020): Earth’s Impact Events Through Geologic Time: A List of Recommended Ages for Terrestrial Impact Structures and Deposits. Astrobiology, 20(1), 91–141. Online: https://doi.org/10.1089/ast.2019.2085 YAU, Kevin – WEISSMAN, Paul – YEOMANS, Donald (1994): Meteorite Falls in China and Some Related Human Casualty Events. Meteoritics, 29(6), 864–871. Online: https://adsabs.harvard.edu/full/1994Metic..29..864Y " ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "103-123" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.7" ["abstract"]=> array(2) { ["en_US"]=> string(1454) "

The paper introduces evaluation methods for the impact risk of near-Earth objects (NEOs), with a particular focus on the Apophis asteroid—a prime example of the current challenges in planetary defense. The paper explains the workings of the traditional Torino and Palermo scales. Additionally, the Cosmic Impact Risk Assessment Scale (CIRAS) is introduced, which refines risk assessment by integrating seven key factors—energy, impact probability, remaining time until impact, impact location, atmospheric effects, secondary hazards, and mitigation difficulty. The close approach of Apophis in 2029 serves as an exceptional case study. While the initial uncertain orbital data suggested a high risk, the most recent measurements indicate an almost negligible chance of collision. The study emphasizes that the combined use of these scales not only allows for clearer communication of risks but also supports the development of targeted planetary defense strategies. This new approach enables more reliable forecasts, contributing to improved social and technological preparedness as well as the timely detection of potential hazards. Overall, the research underlines the importance of detailed, multidimensional risk assessments that facilitate swift responses and the implementation of preventive measures, thereby ensuring Earth’s safety against potential cosmic threats. This approach opens new horizons in the field of planetary defense.

" ["hu_HU"]=> string(1719) "

A cikk a földközeli objektumok (near earth objects, NEO) becsapódási kockázatának értékelési módszereit mutatja be, különös tekintettel az Apophis aszteroidára, amely a bolygóvédelem egyik aktuális kihívása. Először ismertetjük a hagyományos Torino- és Palermo-skálákat. Majd bevezetni javaslunk egy kozmikus becsapódás kockázatbecslő skálát: a „Cosmic Impact Risk Assessment Scale” (CIRAS-) skálát, amely hét kulcsfontosságú tényező – energia, becsapódási valószínűség, hátralévő idő, becsapódási helyszín, atmoszferikus hatás, másodlagos veszélyek és elhárítási nehézség – integrált értékelésével finomítja a kockázat meghatározását. Az Apophis aszteroida 2029-es földközelsége kivételes példaként szolgál, hiszen a kezdeti, bizonytalan pályaadatok magas kockázatot sugalltak, míg a legfrissebb mérések a szinte elhanyagolható ütközési esélyt mutatják. A skálák kombinált alkalmazása nem csupán a kockázatok átláthatóbb kommunikációját teszi lehetővé, hanem támogatja a célzott bolygóvédelmi stratégiák kialakítását is. Az új megközelítés révén megbízhatóbb előrejelzések készíthetők, amelyek hozzájárulhatnak a társadalmi és technológiai felkészültség javításához, valamint a potenciális veszélyek időben történő felismeréséhez. A részletes, többdimenziós kockázatértékelés hozzájárulhat a gyors reagáláshoz és a megelőző intézkedések megtételéhez, így biztosítva a Föld (lakóinak) biztonságát a kozmoszból érkező potenciális veszélyekkel szemben. Ez a megközelítés új távlatokat nyithat a planetáris védelem területén.

" } ["subtitle"]=> array(2) { ["en_US"]=> string(74) "The Place of the Apophis Asteroid on the Torino, Palermo, and CIRAS Scales" ["hu_HU"]=> string(66) "Az Apophis aszteroida helye a Torino-, Palermo- és CIRAS-skálán" } ["title"]=> array(2) { ["en_US"]=> string(31) "Planetary Defence and NEO Risks" ["hu_HU"]=> string(35) "Bolygóvédelem és NEO-kockázatok" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(32) "Horváth János, Horváth Zsuzsa" } ["locale"]=> string(5) "hu_HU" ["authors"]=> array(2) { [0]=> object(Author)#762 (6) { ["_data"]=> array(15) { ["id"]=> int(10950) ["email"]=> string(45) "horvath_janos@visionarytecheventsolutions.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8545) ["seq"]=> int(1) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0002-4767-9263" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(33) "Visionary Tech & Event Solutions " } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(8) "Horváth" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(6) "János" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#760 (6) { ["_data"]=> array(15) { ["id"]=> int(10949) ["email"]=> string(28) "Horvath.Zsuzsanna@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8545) ["seq"]=> int(1) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0001-7945-3296" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(38) "a:1:{s:5:"hu_HU";s:12:"NKE RTK KVI ";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(8) "Horváth" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(6) "Zsuzsa" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["en_US"]=> array(8) { [0]=> string(17) "planetary defense" [1]=> string(12) "Torino Scale" [2]=> string(18) "near-earth objects" [3]=> string(13) "Palermo Scale" [4]=> string(15) "risk assessment" [5]=> string(18) "impact probability" [6]=> string(14) "energy release" [7]=> string(27) "multidimensional risk index" } ["hu_HU"]=> array(9) { [0]=> string(15) "bolygóvédelem" [1]=> string(23) "földközeli objektumok" [2]=> string(13) "Torino-skála" [3]=> string(14) "Palermo-skála" [4]=> string(21) "kockázatértékelés" [5]=> string(28) "ütközési valószínűség" [6]=> string(21) "energiafelszabadulás" [7]=> string(20) "planetáris védelem" [8]=> string(32) "multidimenziós kockázati index" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#764 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46148) ["id"]=> int(6789) ["isApproved"]=> bool(false) ["locale"]=> string(5) "hu_HU" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8545) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#742 (6) { ["_data"]=> array(28) { ["id"]=> int(8568) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:40" ["primaryContactId"]=> int(11002) ["sectionId"]=> int(56) ["seq"]=> int(2) ["submissionId"]=> int(8443) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(8429) "AL-RAWAS, Ghazi et al. (2024): A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques. Water, 16(14). Online: https://doi.org/10.3390/w16142069 ALKAABI, Khaula – SARFRAZ, Uzma – AL DARMAKI, Saif (2025): A Deep Learning Framework for Flash-Flood-Runoff Prediction: Integrating CNN-RNN with Neural Ordinary Differential Equations (ODEs). Water, 17(9), 1283. Online: https://doi.org/10.3390/w17091283 AYYADEVARA, V. Kishore (2018): Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R. New York: Apress. Online: https://doi.org/10.1007/978-1-4842-3564-5 BLANKA-VÉGI Viktória et al. (2024): Gépi tanulási módszerek az aszály és belvíz monitoring és előrejelzés fejlesztésében. Földrajzi Közlemények, 148(2), 175–181. Online: http://www.foldrajzitarsasag.hu/downloads/reviews/2024/FK_2024_02_175_181_Blanka_etal.pdf BREIMAN, Leo (1996): Bagging Predictors. Machine Learning, 24, 123–140. Online: https://doi.org/10.1007/BF00058655 BUSZTA, Julia et al. (2023): Historical Analysis and Prediction of the Magnitude and Scale of Natural Disasters Globally. Resources, 12(9), 106. Online: https://doi.org/10.3390/resources12090106 Copernicus Emergency Management Service. Online: https://emergency.copernicus.eu/ DAYAL, Kavina – DEO, Ravinesh – APAN, Armando A. (2017): Drought Modelling Based on Artificial Intelligence and Neural Network Algorithms: A Case Study in Queensland, Australia. In: LEAL FILHO, Walter (szerk.): Climate Change Adaptation in Pacific Countries. Cham: Springer, 177–198. Online: https://doi.org/10.1007/978-3-319-50094-2_11 DEB, Kalyanmoy et al. (2002): A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. Online: https://doi.org/10.1109/4235.996017 European Flood Awareness System. Online: https://european-flood.emergency.copernicus.eu/en FIALA Károly et al. (2018): Operatív aszály- és vízhiánykezelő monitoring rendszer. Hidrológiai Közlöny, 98(3), 14–24 Online: https://real.mtak.hu/156046/1/Fiala_et_al2018HidrologiaiKozlony.pdf FREUND, Yoav (1995): Boosting a Weak Learning Algorithm by Majority. New Jersey: AT&T Bell Laboratories. Online: https://doi.org/10.1006/inco.1995.1136 FREUND, Yoav – SCHAPIRE, Robert Elias (1996): Experiments with a New Boosting Algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, 1996. Online: https://cseweb.ucsd.edu/~yfreund/papers/boostingexperiments.pdf FRIEDMAN, Jerome Harold (2001): Greedy Function Approximation: A Gradient Boosting Machine. IMS 1999 Reitz Lecture. Online: https://doi.org/10.1214/aos/1013203451 HEGEDŰS Gergely (2020): Újabb mérföldkőhöz érkezett Magyarország árvízvédelme. Magyar Építők, 2020. szeptember 24. Online: https://magyarepitok.hu/vizgazdalkodas/2020/09/ujabb-merfoldkohoz-erkezett-magyarorszag-arvizvedelme JANSEN, Stefan (2021): Machine Learning for Algorithmic Trading. Birmingham: Packt. Online: https://notes.yeshiwei.com/_downloads/2ce792aff8596ea9453a9714f39d957a/Machine_Learning_for_Algorithmic_Trading_Predictive.pdf KIRCSI Andrea et al. (2018): Drought Monitoring in Hungary. Online: https://drmkc.jrc.ec.europa.eu/portals/0/Innovation/SupportSystem/12_Hungary/Documents/thursday/Andrea_Kircsi_final_Hungary20181108.pdf KOTSIANTIS, Sotiris – KANELLOPOULOS, Dimitris (2012): Combining Bagging, Boosting and Random Subspace Ensembles for Regression Problems. International Journal of Innovative Computing, Information and Control, 8(6), 3953–3961. Online: http://www.ijicic.org/ijicic-11-02046.pdf KOZÁK Péter et al. (2022): Az aszály monitoring hálózat és az aszálykezelés gyakorlata a 2022. évi aszály tükrében. Vízügyi Közlemények, 104(3), 113–130. Online: https://library.hungaricana.hu/en/view/VizugyiKozlemenyek_2022/?pg=458 LANG, Bernhard et al. (2008): Neural Clouds for Monitoring of Complex Systems. Optical Memory and Neural Networks, 17(3), 183–192. Online: https://doi.org/10.3103/S1060992X08030016 LIPTAY Zoltán Árpád (2024): Hidrológiai előrejelzés körszimmetrikus bázisfüggvény hálózatokkal. Hidrológiai Közlöny, 98(3), 1–33. Online: https://www.hidrologia.hu/vandorgyules/41/word/0606_liptay_zoltan_arpad.pdf LU, Shuang – HUANG, Jianyun – WU, Jing (2023): Knowledge Domain and Development Trend of Urban Flood Vulnerability Research: A Bibliometric Analysis. Water, 15(10), 1865. Online: https://doi.org/10.3390/w15101865 Magyarország 2021. évi árvízkockázat-kezelési terve (2022). Online: https://vizeink.hu/wp-content/uploads/2022/10/akk/Arvizkockazat-kezelesi_terv.pdf Magyarország Mesterséges Intelligencia Stratégiája (2025–2030) (2025). Online: https://cdn.kormany.hu/uploads/document/c/c0/c0d/c0dfdbd37cfa520ae37361a168d244c85e7295af.pdf MELESE, Tadele et al. (2025): Machine Learning-based Drought Prediction Using Palmer Drought Severity Index and TerraClimate Data in Ethiopia. PLOS ONE, 2025. június 18. Online: https://doi.org/10.1371/journal.pone.0326174 MOKHTAR, Ali et al. (2021): Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms. ACCESS, 9, 65503–65523. Online: https://doi.org/10.1109/ACCESS.2021.3074305 MOSAVI, Amir – OZTURK, Pinar – CHAU, Kwok-wing (2018): Flood Prediction Using Machine Learning Models: Literature Review. Water, 10(11), 1536. Online: https://doi.org/10.3390/w10111536 NEARING, Grey et al. (2024): Global Prediction of Extreme Floods in Ungauged Watersheds. Nature, 627, 559–563. Online: https://doi.org/10.1038/s41586-024-07145-1 NEVO, Sella et al. (2022): Flood Forecasting with Machine Learning Models in an Operational Framework. Hydrology and Earth System Sciences, 26(15), 4013–4032. Online: https://doi.org/10.5194/hess-26-4013-2022 OpenAlex lekérdezés – flood prediction. Online: https://explore.openalex.org/works?page=1&filter=title_and_abstract.search:flood+prediction,type:types/article,primary_topic.id:t11490 OpenAlex lekérdezés – 2000–2025, drought prediction. Online: https://explore.openalex.org/works?page=1&filter=publication_year:2000-2025,title_and_abstract.search:drought+prediction,primary_topic.id:t11490,type:types/article&view=api,report,list OPITZ, David – MACLIN, Richard (1999): Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research, 11, 169–198. Online: https://doi.org/10.1613/jair.614 OYOUNALSOUD, Mhamd Saifaldeen et al. (2024): Drought Prediction Using Artificial Intelligence Models Based on Climate Data and Soil Moisture. Scientific Reports, 14, Article number: 19700. Online: https://doi.org/10.1038/s41598-024-70406-6 PYAYT Alexander et al. (2011): Machine Learning Methods for Environmental Monitoring and Flood Protection. World Academy of Science, Engineering and Technology, 54, 118–123. Online: https://www.researchgate.net/publication/254762064 SAYERS William et al. (2014): Artificial Intelligence Techniques for Flood Risk Management in Urban Environments. Procedia Engineering, 70, 1505–1512. Online: https://doi.org/10.1016/j.proeng.2014.02.165 SCHAPIRE, Robert Elias (1990): The Strength of Weak Learnability. Machine Learning, 5, 197–227. Online: https://doi.org/10.1023/A:1022648800760 SZABÓ János Adolf – LUCZA Zoltán – SZABÓ-MÁRKU Melinda (202l): Mesterséges Intelligencia (MI) alkalmazásának lehetőségei az árvízi előrejelzések pontosításában. HYDROInform. Online: https://hidrologia.hu/vandorgyules/41/word/0607_lucza_zoltan.pdf TAN, Ling et al. (2021): Can We Detect Trends in Natural Disaster Management With Artificial Intelligence? A Review of Modeling Practices. Natural Hazards, 107, 2389–2417. Online: https://doi.org/10.1007/s11069-020-04429-3 Vizet a tájba. Online: https://www.ovf.hu/jobboldali-sav-tartalmai/vizetatajba/vizet-a-tajba WAHBA, Mohamed et al. (2023): A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach. Water, 15(23), 4138. Online: https://doi.org/10.3390/w15234138 ZHOU, Shuyan (2025): Application of AI in Urban Flash Flood Risk Assessment: From Real-time Warning to Resilience Planning. Applied and Computational Engineering, 150(1), 9–14. Online: https://doi.org/10.54254/2755-2721/2025.22398" ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "125-147" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.8" ["abstract"]=> array(2) { ["en_US"]=> string(1738) "

In the last decades, the number of disasters has increased significantly, both natural and man-made, at the national and international level. As a result, the importance of forecasting systems has greatly increased. Information technology and computer science, including artificial intelligence and machine learning, have undergone immense development in recent years, hence making it possible to create predictive models with very high accuracy and great generalization capability, even having relatively few resources available. Due to climate change, the weather is becoming increasingly unpredictable, with extreme weather becoming more frequent. This is a serious challenge for countries, including Hungary. Unfortunately, hydrological disasters have become quite regular in our country. For example, we can mention the Danube and Tisza floods of 2006, the Danube flood of 2013, the major flood hazard situation on the Danube of 2024, the 2025 flood on the Kapos River, the 2013 Zemplén flash flood, the 2020 South Zala flash flood, the very severe droughts of 2012 and 2022, the moderately severe drought of 2021, the dry weather conditions of 2025, and the snow disaster of 2013. In 2024, forecasting systems successfully detected the danger in time, and thanks to extraordinary cooperation and the tireless work of experts, the country managed to defend effectively against the flood. Consequently, taking action in time is crucial, along with the development of new, even more accurate forecasting and warning systems, as well as defense mechanisms and population preparedness. In this article, I present the algorithms used in domestic and international research, as well as the forecasting systems operating in Hungary.

" ["hu_HU"]=> string(1876) "

Az elmúlt évtizedekben jelentősen megnőtt a katasztrófák száma, mind a természeti, mind az ember által előidézett katasztrófáké, hazai és nemzetközi viszonylatban is, ezért az előrejelző rendszerek fontossága felértékelődött. Az informatika és a számítástechnika, ezen belül a mesterséges intelligencia, gépi tanulás ugrásszerű fejlődésen ment keresztül az elmúlt időszakban, így lehetővé téve, relatív kevés erőforrással is, nagyon pontos, magas általánosító képességgel rendelkező prediktív modellek megalkotását. A klímaváltozás miatt az időjárás egyre kiszámíthatatlanabbá válik, egyre gyakoribbak a szélsőséges időjárási jelenségek. Ez komoly nehézségek elé állítja az országokat, beleértve Magyarországot is. A hidrológiai katasztrófák hazánkban, a vízrajzi és a domborzati adottságokból adódóan, mindig jellemző katasztrófakockázatot jelentettek. Megemlíthetjük az elmúlt évtizedekből például a 2006-os dunai és tiszai árvizet, a 2013-as dunai árvizet, a 2024-es nagy dunai árhullámot, a 2025-ös áradást a Kaposon, a 2013-as zempléni, a 2020-as dél-zalai villámárvizet, a 2012-es, a 2022-es rendkívüli aszályokat, a 2021-es csapadékhiányt, a 2025-ös aszályos időjárást, a 2013-as hókatasztrófát. 2024-ben az előrejelző rendszereknek sikerült időben érzékelni a veszélyt, a rendkívüli összefogásnak és a szakemberek fáradhatatlan munkájának köszönhetően sikerült hatékonyan védekezni az árvíz ellen. Tehát fontos az időbeni cselekvés, új, még pontosabb előrejelző, riasztó rendszerek és védelmi mechanizmusok kialakítása, a lakosság felkészítése. Ebben a cikkben összefoglaljuk a hazai és a nemzetközi kutatásokban használt algoritmusokat és a hazánkban működő előrejelző rendszereket.

" } ["title"]=> array(2) { ["en_US"]=> string(115) "Application of Artificial Intelligence and Machine Learning Algorithms in Protection Against Hydrological Disasters" ["hu_HU"]=> string(128) "A mesterséges intelligencia és gépi tanulás algoritmusainak alkalmazása a hidrológiai katasztrófák elleni védekezésben" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(38) "Kirovné Rácz Réka, Scholtz Emánuel" } ["locale"]=> string(5) "hu_HU" ["authors"]=> array(2) { [0]=> object(Author)#778 (6) { ["_data"]=> array(15) { ["id"]=> int(11003) ["email"]=> string(28) "kirovne.racz.reka@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8568) ["seq"]=> int(2) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0001-8818-2539" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(6) "Magyar" ["hu_HU"]=> string(31) "Nemzeti Közszolgálati Egyetem" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(14) "Kirovné Rácz" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(5) "Réka" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#745 (6) { ["_data"]=> array(15) { ["id"]=> int(11002) ["email"]=> string(27) "scholtz.emanuel99@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8568) ["seq"]=> int(2) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0009-0000-9320-3437" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(57) "a:1:{s:5:"hu_HU";s:31:"Nemzeti Közszolgálati Egyetem";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(7) "Scholtz" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(8) "Emánuel" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(14) { [0]=> string(26) "mesterséges intelligencia" [1]=> string(20) "katasztrófavédelem" [2]=> string(7) "árvíz" [3]=> string(7) "aszály" [4]=> string(14) "gépi tanulás" [5]=> string(15) "árvízvédelem" [6]=> string(20) "településelöntés" [7]=> string(14) "villámárvíz" [8]=> string(11) "informatika" [9]=> string(17) "neurális hálók" [10]=> string(22) "support vector machine" [11]=> string(13) "decision tree" [12]=> string(13) "random forest" [13]=> string(11) "fuzzy logic" } ["en_US"]=> array(15) { [0]=> string(23) "artificial intelligence" [1]=> string(16) "machine learning" [2]=> string(19) "disaster management" [3]=> string(5) "flood" [4]=> string(16) "flood protection" [5]=> string(14) "urban flooding" [6]=> string(11) "flash flood" [7]=> string(15) "inland flooding" [8]=> string(7) "drought" [9]=> string(22) "information technology" [10]=> string(15) "neural networks" [11]=> string(22) "support vector machine" [12]=> string(13) "decision tree" [13]=> string(13) "random forest" [14]=> string(11) "fuzzy logic" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#772 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46149) ["id"]=> int(6790) ["isApproved"]=> bool(false) ["locale"]=> string(5) "hu_HU" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8568) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#765 (6) { ["_data"]=> array(28) { ["id"]=> int(8637) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:39" ["primaryContactId"]=> int(11118) ["sectionId"]=> int(56) ["seq"]=> int(3) ["submissionId"]=> int(8512) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(3975) "BARANYAI Gábor – CSERNUS Dóra Ildikó szerk. (2018): A fenntartható fejlődés és az állam feladatai. Budapest: Dialog Campus. Online: https://vtk.uni-nke.hu/document/vtk-uni-nke-hu/webXS_PDF_ATMA_Fenntarthato_fejlodes.pdf BM – Országos Katasztrófavédelmi Főigazgatóság [é. n. a.]: Lakosságfelkészítés. Online: https://www.katasztrofavedelem.hu/62/lakossagfelkeszites BM – Országos Katasztrófavédelmi Főigazgatóság [é. n. b.]: Környezetvédelem és kéményseprés. Online: https://kemenysepres.katasztrofavedelem.hu/tajekoztatok/kornyved EUR-Lex [é. n.]: Glosszárium az összefoglalókhoz – Fenntartható fejlődés. Online: https://eur-lex.europa.eu/legal-content/HU/TXT/?uri=LEGISSUM:sustainable_development ÉRCES Gergő – VASS Gyula – AMBRUSZ József (2023): Épületek károsító hatásokkal szembeni rezilienciájának jellemzői. Polgári Védelmi Szemle, 15(különszám), 117–130. Online: https://kvi.uni-nke.hu/document/kvi-uni-nke-hu/%C3%96sszerakott%20PV%20Szemle%20cikkek_02_24_Szerkesztett.pdf#page=117 HOFFMANN Imre et al. (2015): Iparbiztonság Magyarországon. Védelem Online: Tűz- és Katasztrófavédelmi Szakkönyvtár, 22(1). Online: https://www.vedelem.hu/letoltes/anyagok/549-dr-hoffmann-imre-dr-levai-zoltan-dr-katai-urban-lajos-dr-vass-gyula.pdf International Energy Agency (2025): Global Energy Review 2025. Online: https://iea.blob.core.windows.net/assets/5b169aa1-bc88-4c96-b828-aaa50406ba80/GlobalEnergyReview2025.pdf ITM – Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (2021): Magyarország kutatási, fejlesztési és innovációs stratégiája 2021–2030. ITM. Online: https://nkfih.gov.hu/hivatalrol/strategia-alkotas/kutatasi-fejlesztesi-innovacios-strategia KÁTAI-URBÁN Lajos (2014): Súlyos ipari balesetek megelőzését és a felkészülést célzó jogintézmények egységes rendszerbe foglalása. Hadmérnök, 9(4), 94–105. Online: http://www.hadmernok.hu/144_10_katai_urbanl_1.pdf Nemzeti Fenntartható Fejlődési Tanács (2012): Nemzeti Fenntartható Fejlődési Keretstratégia 2012–2024. A 18/2013. (III. 28.) OGY határozat melléklete. Online: https://njt.hu/jogszabaly/2013-18-30-41 NEUFELD, Dorothy (2025): Chart: What Powered the World in 2024? Visual Capitalist, 2025. augusztus 22. Online: https://www.visualcapitalist.com/what-powered-the-world-in-2024/ SIBALIN Iván (2022): Az energetikai rendszerek fenntartható működésével kapcsolatos iparbiztonsági tevékenységek stratégiai célú kutatása és fejlesztése. PhD-értekezés. Budapest: NKE Katonai Műszaki Doktori Iskola. Online: https://tudasportal.uni-nke.hu/xmlui/bitstream/handle/20.500.12944/19565/sibalin_ivan_doktori_ertekezes.pdf SZAKÁL Béla et al. szerk. (2020): Módszertani kézikönyv a veszélyes anyagokkal kapcsolatos súlyos balesetek elleni védekezéssel foglalkozó gyakorló szakemberek részére. Budapest: Hungária Veszélyesáru Mérnöki Iroda. TEKNŐS László – KÓRÓDI Gyula (2016): A vízzel kapcsolatos veszélyeztetettség éghajlatváltozással kapcsolatos aspektusainak katasztrófavédelmi szempontú elemzése és kiértékelése II. Hadmérnök, 11(3), 83–96. Online: http://hadmernok.hu/163_07_teknos.pdf United Nations Educational, Scientific and Cultural Organization (2020): Education for Sustainable Development: a Roadmap. Párizs: UNESCO. Online: https://doi.org/10.54675/YFRE1448 Vidékfejlesztési Minisztérium (2011): Nemzeti Környezettechnológiai Innovációs Stratégia 2011–2020. 1307/2011. (IX. 6.) Korm. határozat melléklete. Online: http://njt.hu/cgi_bin/njt_doc.cgi?docid=140750.204955 World Economic Forum (2025): The Global Risks Report 2025. 20th Edition. Geneva. Online: https://reports.weforum.org/docs/WEF_Global_Risks_Report_2025.pdf Jogi forrás 1163/2020. (IV. 21.) Korm. határozat Magyarország Nemzeti Biztonsági Stratégiájáról. 1. melléklet. Online: https://njt.hu/jogszabaly/2020-1163-30-22 " ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "149-159" ["pub-id::doi"]=> string(20) "10.32567/hm.2025.4.9" ["abstract"]=> array(2) { ["en_US"]=> string(664) "

Ensuring the safe operation of hazardous facilities through the effective implementation of industrial safety measures is a fundamental prerequisite for achieving sustainable development. The primary objective of the present study is to explore the interrelations between the industrial safety in the energy sector and the environmental dimension. Following the identification of theoretical and practical arguments supporting the strategic importance of industrial safety in sustainable development, the first part of this article series evaluates the position of industrial safety in the energy sector within the discourse on environmental sustainability.

" ["hu_HU"]=> string(644) "

A veszélyes anyagokkal foglalkozó üzemek biztonságos működését célzó iparbiztonsági szempontok hatékony érvényesülése a fenntartható fejlődési stratégia sikerének alapvető feltétele. Jelen kutatás fő célja az energiaipari-biztonság és a környezeti dimenzió közötti összefüggések feltárása. A cikksorozat első része az iparbiztonság fenntartható fejlődésben betöltött stratégiai jelentőségét megalapozó elméleti és gyakorlati érvek meghatározását követően értékeli az energiaágazat üzembiztonsága kérdésének a környezeti fenntarthatósági diskurzusban elfoglalt helyét.

" } ["title"]=> array(2) { ["en_US"]=> string(124) "Assessment of Certain Interrelations between Industrial Safety in the Energy Sector and Environmental Sustainability, Part 1" ["hu_HU"]=> string(112) "Az energiaipari-biztonság és a környezeti fenntarthatóság egyes összefüggéseinek értékelése, 1. rész" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(47) "Sibalin Iván, Kátai-Urbán Maxim, Cimer Zsolt" } ["locale"]=> string(5) "hu_HU" ["authors"]=> array(3) { [0]=> object(Author)#787 (6) { ["_data"]=> array(15) { ["id"]=> int(11118) ["email"]=> string(18) "sibalin4@gmail.com" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8637) ["seq"]=> int(3) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0002-7228-6832" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(28) "a:1:{s:5:"hu_HU";s:3:"NKE";}" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(7) "Sibalin" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(5) "Iván" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [1]=> object(Author)#788 (6) { ["_data"]=> array(15) { ["id"]=> int(11119) ["email"]=> string(31) "katai.urban.maxim@semmelweis.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8637) ["seq"]=> int(3) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0001-5079-4644" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(13) "Kátai-Urbán" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(5) "Maxim" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } [2]=> object(Author)#768 (6) { ["_data"]=> array(15) { ["id"]=> int(11120) ["email"]=> string(22) "cimer.zsolt@uni-nke.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8637) ["seq"]=> int(3) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0000-0001-6244-0077" ["url"]=> string(0) "" ["affiliation"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(5) "Cimer" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(5) "Zsolt" } ["preferredPublicName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(5) { [0]=> string(14) "iparbiztonság" [1]=> string(14) "energiaágazat" [2]=> string(29) "környezeti fenntarthatóság" [3]=> string(7) "baleset" [4]=> string(29) "veszélyhelyzeti kibocsátás" } ["en_US"]=> array(5) { [0]=> string(17) "industrial safety" [1]=> string(13) "energy sector" [2]=> string(28) "environmental sustainability" [3]=> string(8) "accident" [4]=> string(17) "emergency release" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#780 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46151) ["id"]=> int(6791) ["isApproved"]=> bool(false) ["locale"]=> string(5) "hu_HU" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8637) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }

object(Publication)#116 (6) { ["_data"]=> array(29) { ["id"]=> int(8591) ["accessStatus"]=> int(0) ["datePublished"]=> string(10) "2026-05-12" ["lastModified"]=> string(19) "2026-05-12 12:05:40" ["primaryContactId"]=> int(11034) ["sectionId"]=> int(58) ["seq"]=> int(1) ["submissionId"]=> int(8466) ["status"]=> int(3) ["version"]=> int(1) ["categoryIds"]=> array(0) { } ["citationsRaw"]=> string(13943) "Az Európai Parlament és a Tanács (EU) 2021/697 rendelete (2021. április 29.) az Európai Védelmi Alap létrehozásáról és az (EU) 2018/1092 határozat hatályon kívül helyezéséről (2024). Az Európai Hivatalos Lapja, L 170, 149–187. Online: https://op.europa.eu/hu/publication-detail/-/publication/d94a6745-ebaf-11ee-8e14-01aa75ed71a1/language-hu?utm_ BATEMAN, Jon (2022): U.S. – China Technological „Decoupling”: A Strategy and Policy Framework. Washington, DC: Carnegie Endowment for International Peace Publications Department. Online: https://carnegieendowment.org/research/2022/04/us-china-technological-decoupling-a-strategy-and-policy-framework?lang=en#the-evolution-of-us-thinking-and-policy BEAVER, Wilson (2025): China’s Defense Budget Is Bigger Than You Think. The Heritage Foundation, 2025. április 17. Online: https://www.heritage.org/china/commentary/chinas-defense-budget-bigger-you-think?utm_ BICKELL, Eleni G. et al. (2024): Federal Research and Development (R&D) Funding: FY2025. CRS Report. Online: https://www.congress.gov/crs-product/R48307?utm= BLEVINS, Emily G. (2025): Federal Research Security Policies: Background and Issues for Congress. CRS Report. Online: https://www.congress.gov/crs-product/R48541 BODA Mihály (2022): A hibrid háború etikája: az igazságos hibrid háború elmélete. In M. SZABÓ Miklós (szerk.): A hadtudomány aktuális kérdései napjainkban I. Budapest: Ludovika, 95–108. Online: https://doi.org/10.36250/00973_06 BODA Mihály (2025): A multipoláris nemzetközi rendtől a poliarchikusig – a „hadszíntérháború”-tól a hibrid háborúig. Kézirat. CHAN, Kyle et al. (2025): Full Stack: China's Evolving Industrial Policy for AI. RAND Expert Insights, 2025. június 26. Online: https://doi.org/10.7249/PEA4012-1 China Aerospace Studies Institute (CASI) (2025): In Their Own Words: 2025 China's National Security in the New Era. [H. n.]: China Aerospace Studies Institute. Online: https://bit.ly/46AGWk4 CLAPP, Sebastian (2022): Emerging Disruptive Technologies in Defence. European Parliamentary Research Service. Online: https://www.europarl.europa.eu/thinktank/en/document/EPRS_ATA(2022)733647 Defense Advanced Research Projects Agency (DARPA) (2022): DARPA Seeks Proposals to Forge the Future of U.S. Microelectronics Manufacturing. Online: https://www.darpa.mil/news/2022/future-microelectronics-manufacturing DING, Jeffrey – XIAO, Jenny W. (2023): Recent Trends in China’s Large Language Model Landscape. Centre for the Governance of AI. Online: https://cdn.governance.ai/Trends_in_Chinas_LLMs.pdf FANG, Tianyu – HWANG, Tim (2023): The Rise of Techno-Nationalism. Online: https://www.newamerica.org/oti/reports/the-rise-of-techno-nationalism/ FEAKIN, Tobias – SEGAL, Adam (2025): Brave New Techno-Nationalist World. Foreign Policy, 2025. június 4. Online: https://foreignpolicy.com/2025/06/04/trump-tech-policy-techno-nationalism/?utm_ FETTER, Steve – SANKARAN, Jaganath (2024): Emerging Technologies and Challenges to Nuclear Stability. Journal of Strategic Studies, 48(2), 252–296. Online: https://doi.org/10.1080/01402390.2024.2433766 GALLO, Marcy E. (2021): Defense Advanced Research Projects Agency: Overview and Issues for Congress. CRS Report. Online: https://sgp.fas.org/crs/natsec/R45088.pdf GORECZKY Péter (2024): Nyitottnak maradni, de mennyire – erősödő techno-nacionalizmus a világban. Világgazdaság, 2024. július 29. Online: https://www.vg.hu/velemeny/2024/07/nyitottnak-maradni-de-mennyire-erosodo-techno-nacionalizmus-a-vilagban?utm= HADDAD, Christian – VORLÍČEK, Dagmar – KLIMBURG-WITJES, Nina (2024): The Security-Innovation Nexus in (Geo-) Political Imagination. Geopolitics, 29(3), 741–764. Online: https://doi.org/10.1080/14650045.2024.2329940 HÁBER Péter (2022): A hibrid hadviselés elméletének tömör összefoglalása a hazai szakirodalom alapján. Felderítő Szemle, 21(4), 72–94. Online: https://hhk.uni-nke.hu/document/hhk-uni-nke-hu/2022_4_Felder%C3%ADt%C5%91%20Szemle.pdf HOROWITZ, Michael C. – KAHN, Lauren (2021): DoD's 2021 China Military Power Report: How Advances in AI and Emerging Technologies Will Shape China’s Military. Council on Foreign Relations. Online: https://www.cfr.org/blog/dods-2021-china-military-power-report-how-advances-ai-and-emerging-technologies-will-shape KANIA, Elsa B. (2019): Minds at War. China’s Pursuit of Military Advantage through Cognitive Science and Biotechnology. PRISM, 8(3), 82–101. Online: https://www.jstor.org/stable/pdf/26864278.pdf?refreqid=fastly-default%3A246290d30dc0d9c005b36c4079a51dd5&ab_segments=&initiator=&acceptTC=1 KANIA, Elsa B. et al. (2021): How Should the U.S. Respond to China’s Military-Civil Fusion Strategy? ChinaFile, 2021. május 22. Online: https://www.chinafile.com/conversation/how-should-us-respond-chinas-military-civil-fusion-strategy KISVÁRI Tamás (2024): Kína hosszú távú stratégiája. In GÖRBE Attiláné – ZÁN Krisztina (szerk.): Kína – A Középső Birodalom. Budapest: Katonai Nemzetbiztonsági Szolgálat, 204–225. Online: https://bit.ly/4uaMWdC KRACH, Keith – CHIANG, Mung – ASHLEY FORD, Christopher (2020): Briefing on Taiwan Semiconductor Manufacturing Corporation’s Intent To Invest $12 Billion in the U.S. and on the CCP’s Ability To Undermine U.S. Export Controls. Online: https://bit.ly/4bjlxOW LEE, Ji-Young – HAN, Eugeniu – ZHU, Keren (2022): Decoupling from China: How U.S. Asian Allies Responded to the Huawei ban. Australian Journal of International Affairs, 76(5), 486–506. Online: https://doi.org/10.1080/10357718.2021.2016611 LEE, Lizzi C. (2024): China’s Big Fund 3.0: Xi’s Boldest Gamble Yet for Chip Supremacy. The diplomat, 2024. június 6. Online: https://thediplomat.com/2024/06/chinas-big-fund-3-0-xis-boldest-gamble-yet-for-chip-supremacy/ LIANG, Xiao et al. (2025): Trends in World Military Expenditure, 2024. SIPRI Publications, 1–12. Online: https://doi.org/10.55163/AVEC8366 MCFAUL, Cole – BRESNICK, Sam – CHOU, Daniel (2025): Pulling Back the Curtain on China’s Military-Civil Fusion. How the PLA Mobilizes Civilian AI for Strategic Advantage. CSET, 2025. szeptember. Online: https://cset.georgetown.edu/wp-content/uploads/CSET-Pulling-Back-the-Curtain-on-Chinas-Military-Civil-Fusion.pdf MOK, Charles (2023): The Party Rules: China’s New Central Science and Technology Commission. The Diplomat, 2023. augusztus 23. Online: https://thediplomat.com/2023/08/the-party-rules-chinas-new-central-science-and-technology-commission/ MOSLEY, Brian (2024): FY24 Budget Update. CRA News, 36(4). Online: https://bit.ly/40GNXwe National Bureau of Statistics of China (NBS) (2025): China’s Expenditure on Research and Experimental Development (R&D) Exceeded 3.6 Trillion Yuan in 2024. Online: https://www.stats.gov.cn/english///PressRelease/202502/t20250207_1958579.html?utm Nature (2020): Editorial: DARPA ‘Lookalikes’ Must Ground Their Dreams in Reality. Nature, 579, 173–174. Online: https://doi.org/10.1038/d41586-020-00690-5 O’HANLON, Michael E. (2025): Achieving „Peace through Strength” in the 2020s. Brookings, 2025. február 21. Online: https://www.brookings.edu/articles/achieving-peace-through-strength-in-the-2020s/?utm PANYUE, Huang szerk. (2022): Xi's Vision Leads to Success. Online: http://eng.mod.gov.cn/xb/News_213114/TopStories/4923634.html?utm REDING, D. F. – EATON, J. (2020): Science & Technology Trends 2020–2040. Brussels: NATOScience & Technology Organization Office of the Chief Scientist NATOHeadquarters. Online: https://apps.dtic.mil/sti/pdfs/AD1131124.pdf REMEK Éva (2023): Változó biztonság, változó biztonságfelfogás. In: ZACHAR Péter Krisztián – BARNA Attila (szerk.): Titkos cikkek az örök békéhez. Ünnepi tanulmányok a 70 éves Fülöp Mihály tiszteletére. Budapest: Ludovika, 337–349. Online: https://doi.org/10.36250/01132_27 SARGENT, John F. – SHEA, Dana A. (2020): Office of Science and Technology Policy (OSTP): History and Overview. Online: https://www.congress.gov/crs-product/R43935 SHERMAN, Justin (2019): Essay: Reframing the U.S.China AI „Arms Race”. Online: https://www.newamerica.org/cybersecurity-initiative/reports/essay-reframing-the-us-china-ai-arms-race/introduction Silk Road Hungary (2025): Kína tudományos és technológiai fejlődése új lendületet ad az ipari innovációnak. 2025. július 28. Online: https://silkroadhungary.hu/2025/07/kina-tudomanyos-es-technologiai-fejlodese-uj-lenduletet-ad-az-ipari-innovacionak-2/ SOARE, Simona R. – POTHIER, Fabrice (2021): Leading Edge: Key Drivers of Defence Innovation and the Future of Operational Advantage. International Institute of Strategic Studies Research Papers. Online: https://www.iiss.org/globalassets/media-library---content--migration/files/research-papers/2021/11/defence-innovation_oct-2021_s10.pdf SULLIVAN, Ian M. (2024): Three Dates, Three Windows, and All of DOTMLPF-P. Military Rewiew, 104(1), 14–25. Online: https://www.armyupress.army.mil/Journals/Military-Review/English-Edition-Archives/January-February-2024/Sullivan/Journals/Military-Review/utm/ SZÖLLŐSI Annamária (2025): A reziliencia a biztonság kontextusában. Hadtudományi Szemle, 18(2), 161–177. Online: https://doi.org/10.32563/hsz.2025.2.10 The White House (2022): National Security Strategy. Online: https://bidenwhitehouse.archives.gov/wp-content/uploads/2022/10/Biden-Harris-Administrations-National-Security-Strategy-10.2022.pdf The White House (2025a): President Trump is Leading with Peace Through Strength. Online: https://www.whitehouse.gov/articles/2025/03/president-trump-is-leading-with-peace-through-strength/?utm The White House (2025b): Fact Sheet: President Donald J. Trump Restores the United States Department of War. Online: https://www.whitehouse.gov/fact-sheets/2025/09/fact-sheet-president-donald-j-trump-restores-the-united-states-department-of-war/ The White House (2025c): President’s Council of Advisors on Science and Technology. Online: https://www.whitehouse.gov/presidential-actions/2025/01/presidents-council-of-advisors-on-science-and-technology/ The White House [é. n.]: Office of Science and Technology Policy. Online: https://www.whitehouse.gov/ostp/?utm TIAN, Nan et al. (2024): Trends in World Military Expenditure, 2023. SIPRI Fact Sheet, 2024. április. Online: https://www.sipri.org/sites/default/files/2024-04/2404_fs_milex_2023.pdf U.S. Department of Defense (2022): National Defense Strategy of the United States of America. Online: https://media.defense.gov/2022/Oct/27/2003103845/-1/-1/1/2022-NATIONAL-DEFENSE-STRATEGY-NPR-MDR.PDF U.S. Department of Defense (2023): National Defense Industrial Strategy. Online: https://www.govinfo.gov/content/pkg/GOVPUB-D-PURL-gpo234254/pdf/GOVPUB-D-PURL-gpo234254.pdf U.S. Department of Defense (2024a): Military and Security Developments Involving the People’s Republic of China. Online: https://media.defense.gov/2024/Dec/18/2003615520/-1/-1/0/MILITARY-AND-SECURITY-DEVELOPMENTS-INVOLVING-THE-PEOPLES-REPUBLIC-OF-CHINA-2024.PDF U.S. Department of Defense (2024b): National Defense Industrial Strategy Implementation Plan for FY2025. Online: https://www.govinfo.gov/content/pkg/GOVPUB-D-PURL-gpo234260/pdf/GOVPUB-D-PURL-gpo234260.pdf U.S. Department of War (2023): Chief Digital & Artificial Intelligence Office Celebrates First Year. Online: https://www.war.gov/News/Releases/Release/Article/3464012/chief-digital-artificial-intelligence-office-celebrates-first-year/ U.S. Department of War (2024): CDAO and DIU Launch New Effort Focused on Accelerating DOD Adoption of AI Capabilities. Online: https://www.war.gov/News/Releases/Release/Article/3996199/cdao-and-diu-launch-new-effort-focused-on-accelerating-dod-adoption-of-ai-capab/ U.S. Department of War (2025a): Statement on the Development of the 2025 National Defense Strategy. Online: https://www.war.gov/News/Releases/Release/Article/4172735/statement-on-the-development-of-the-2025-national-defense-strategy/ U.S. Department of War (2025b): DIU’s Thunderforge Project to Integrate Commercial AI-Powered Decision-Making for Operational and Theater-Level Planning. Online: https://www.diu.mil/latest/dius-thunderforge-project-to-integrate-commercial-ai-powered-decision-making U.S. Government Accountability Office (GAO) (2024): Hypersonic Weapons: DOD Could Reduce Cost and Schedule Risks by Following Leading Practices. Report to Congressional Committees. Online: https://www.gao.gov/assets/880/870416.pdf VIGLIAROLO, Brandon (2022): Darpa Says US Hypersonic Missile Is Ready for Real World. The Register, 2022. április 6. Online: https://www.theregister.com/2022/04/06/darpa_hypersonic_missile/ VIGLIAROLO, Brandon (2025): It Begins: Pentagon to Give AI Agents a Role in Decision Making, Ops Planning. The Register, 2025. március 5. Online: https://www.theregister.com/2025/03/05/dod_taps_scale_to_bring/?utm VUK, Pavel (2025): Editorial: Disruptive Technologies. Contemporary Military Challenges, 27(1), 13–18. Online: https://doi.org/10.2478/cmc-2025-0002 WONG LEUNG, Jennifer – ROBIN, Stephan – CAVE, Danielle (2024): ASPI’s Two-Decade Critical Technology Tracker: The Rewards of Long-term Research Investment. Barton ATC: ASPI. Online: https://bit.ly/4rPT6hE World Intellectual Property Organization (WIPO) (2025): Global Innovation Index 2025: Innovation at a Crossroads. Genf: WIPO. Online: https://doi.org/10.34667/tind.58864 YAN, Ming (2023): How Techno-Nationalism Affects Technological Decoupling Between China and the U.S. International Journal of Education and Humanities, 10(3), 10–13. Online: https://doi.org/10.54097/ijeh.v10i3.11782 ZHANG, Lin – LAN, Tu (2022): The New Whole State System: Reinventing the Chinese State to Promote Innovation. Environment and Planning A: Economy and Space, 55(1), 201–221. Online: https://doi.org/10.1177/0308518X221088294" ["copyrightYear"]=> int(2026) ["issueId"]=> int(685) ["licenseUrl"]=> string(49) "https://creativecommons.org/licenses/by-nc-nd/4.0" ["pages"]=> string(7) "161-180" ["pub-id::doi"]=> string(21) "10.32567/hm.2025.4.10" ["abstract"]=> array(2) { ["en_US"]=> string(1125) "

In contemporary geopolitical competition, the close interconnection between technology, innovation, and national security is theoretically framed by the concept of techno-nationalism. At the core of these processes lies the great-power strategic rivalry between the United States and the People’s Republic of China, in which defense innovation and the pursuit of technological superiority are regarded as prerequisites for both national security and economic competitiveness. The comparative analysis of defense innovation ecosystems highlights structural differences in national research and development models, as well as the divergent logics of financing and strategic governance. The study further reviews the emergence and theoretical foundations of techno-nationalism and explores its practical manifestations in the context of great-power competition. The analysis demonstrates that efforts toward technological autonomy and strategic sovereignty have become central drivers of geopolitical, economic, and scientific dynamics, shaping the 21st-century global order increasingly along techno-nationalist lines.

" ["hu_HU"]=> string(1183) "

A kortárs geopolitikai versengésben a technológia, az innováció és a nemzetbiztonság szoros összefonódása a technonacionalizmus fogalmában nyer elméleti keretet. E folyamatok középpontjában az Egyesült Államok és a Kínai Népköztársaság nagyhatalmi stratégiai versengése áll, amely a védelmi innovációt és a technológiai fölény megszerzését a nemzetbiztonság és a gazdasági versenyképesség alapfeltételeként értelmezi. A védelmi innovációs ökoszisztémák összehasonlító elemzése rávilágít a nemzeti kutatás-fejlesztési modellek szerkezeti eltéréseire, valamint a finanszírozási mechanizmusok és a stratégiai irányítás eltérő logikáira. A tanulmány áttekinti a technonacionalizmus kialakulását és elméleti alapjait, valamint feltárja annak gyakorlati megnyilvánulásait a nagyhatalmi versengésben. Az elemzés rámutat, hogy a technológiai önállóság és a stratégiai szuverenitás iránti törekvések a geopolitikai, gazdasági és tudományos dinamika központi mozgatórugóivá váltak, és a 21. század világrendje egyre inkább a technonacionalista törekvések mentén formálódik.  

" } ["subtitle"]=> array(2) { ["en_US"]=> string(72) "A Comparative Analysis of U.S. and Chinese Techno-Nationalist Approaches" ["hu_HU"]=> string(81) "Az amerikai és a kínai technonacionalista megközelítések összehasonlítása" } ["title"]=> array(2) { ["en_US"]=> string(22) "Weaponising Innovation" ["hu_HU"]=> string(39) "Az innováció mint stratégiai fegyver" } ["copyrightHolder"]=> array(1) { ["hu_HU"]=> string(21) "Szöllősi Annamária" } ["locale"]=> string(5) "hu_HU" ["authors"]=> array(1) { [0]=> object(Author)#734 (6) { ["_data"]=> array(14) { ["id"]=> int(11034) ["email"]=> string(29) "szollosi.annamaria@outlook.hu" ["includeInBrowse"]=> bool(true) ["publicationId"]=> int(8591) ["seq"]=> int(1) ["userGroupId"]=> int(184) ["country"]=> string(2) "HU" ["orcid"]=> string(37) "https://orcid.org/0009-0005-9021-6829" ["url"]=> string(0) "" ["biography"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(0) "" } ["familyName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(10) "Szöllősi" } ["givenName"]=> array(2) { ["en_US"]=> string(0) "" ["hu_HU"]=> string(10) "Annamária" } ["affiliation"]=> array(1) { ["hu_HU"]=> string(86) "a:1:{s:5:"hu_HU";s:60:"Nemzeti Közszolgálati Egyetem Hadtudományi Doktori Iskola";}" } ["submissionLocale"]=> string(5) "hu_HU" } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) } } ["keywords"]=> array(2) { ["hu_HU"]=> array(5) { [0]=> string(42) "feltörekvő és bomlasztó technológiák" [1]=> string(20) "védelmi innováció" [2]=> string(29) "védelmi kutatás-fejlesztés" [3]=> string(19) "stratégiai verseny" [4]=> string(19) "technonacionalizmus" } ["en_US"]=> array(5) { [0]=> string(36) "Emerging and Disruptive Technologies" [1]=> string(18) "Defense Innovation" [2]=> string(32) "Defense Research and Development" [3]=> string(21) "Strategic Competition" [4]=> string(18) "Techno-nationalism" } } ["subjects"]=> array(0) { } ["disciplines"]=> array(0) { } ["languages"]=> array(0) { } ["supportingAgencies"]=> array(0) { } ["galleys"]=> array(1) { [0]=> object(ArticleGalley)#771 (7) { ["_data"]=> array(9) { ["submissionFileId"]=> int(46153) ["id"]=> int(6792) ["isApproved"]=> bool(false) ["locale"]=> string(5) "hu_HU" ["label"]=> string(3) "PDF" ["publicationId"]=> int(8591) ["seq"]=> int(0) ["urlPath"]=> string(0) "" ["urlRemote"]=> string(0) "" } ["_hasLoadableAdapters"]=> bool(true) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) ["_submissionFile"]=> NULL } } } ["_hasLoadableAdapters"]=> bool(false) ["_metadataExtractionAdapters"]=> array(0) { } ["_extractionAdaptersLoaded"]=> bool(false) ["_metadataInjectionAdapters"]=> array(0) { } ["_injectionAdaptersLoaded"]=> bool(false) }