Autonóm drónrendszerek kiberbiztonsága: kockázatok és reziliencia

Szakirodalmi áttekintés

doi: 10.32561/nsz.2026.1.1

Absztrakt

Az autonóm és részben autonóm pilóta nélküli légi járművek (unmanned aerial vehicle, UAV), valamint az ezek működését integrált módon biztosító pilóta nélküli légijármű-rendszerek (unmanned aircraft system, UAS) gyors térnyerése alapvetően új kiberbiztonsági kihívásokat hívott életre. Az autonóm döntéshozatal, a hálózatalapú kommunikáció és a fizikai működés szoros összekapcsolódása következtében a kibertérben végrehajtott támadások közvetlen és azonnali működési, valamint biztonsági következményekkel járhatnak. A tanulmány szisztematikus szakirodalmi áttekintésre építve elemzi az autonóm UAS-okat érintő kiberbiztonsági kockázatokat, különös hangsúlyt fektetve a navigációs rendszereket célzó GPS-spoofing-fenyegetésekre. Az elemzés bemutatja továbbá azokat a rétegezett védelmi mechanizmusokat és rezilienciafokozó megközelítéseket, amelyek alkalmasak e komplex fenyegetések hatásainak mérséklésére és az autonóm rendszerek üzembiztonságának növelésére.

Kulcsszavak:

autonóm drónrendszerek UAV UAS kiberbiztonság reziliencia GPS spoofing

Hivatkozások

ALDOSSARY, Mohammad – ALZAMIL, Ibrahim – ALMUTAIRI, Jaber (2025): Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications. Drones, 9(1), 46. Online: https://doi.org/10.3390/drones9010046

ALHORAIBI, Lamia – ALGHAZZAWI, Daniyal – ALHEBSHI Reemah (2024): Detection of GPS Spoofing Attacks in UAVs Based on Adversarial Machine Learning Model. Sensors, 24(18), 6156. Online: https://doi.org/10.3390/s24186156

ALMADHOR, Ahmad et al. (2025): CTDNN-Spoof: Compact Tiny Deep Learning Architecture for Detection and Multi-Label Classification of GPS Spoofing Attacks in Small UAVs. Scientific Reports, 15, 6656. Online: https://doi.org/10.1038/s41598-025-90809-3

ALQUWAYZANI, Alanoud A. – ALBUALI, Abdullah A. (2024): A Systematic Literature Review of Zero Trust Architecture for Military UAV Security Systems. IEEE Access, 12, 176033–176056. Online: https://doi.org/10.1109/ACCESS.2024.3503587

ALSADIE, Deafallah (2025): Cybersecurity and Artificial Intelligence in Unmanned Aerial Vehicles: Emerging Challenges and Advanced Countermeasures. IET Information Security, (1), 2046868. Online: https://doi.org/10.1049/ise2/2046868

ASIF, Muneeba et al. (2023): Adversarial Data-Augmented Resilient Intrusion Detection System for Unmanned Aerial Vehicles. In 2023 IEEE International Conference on Big Data (BigData). Sorrento, Italy, 5428–5437. Online: https://doi.org/10.1109/BigData59044.2023.10386140

BABA, Abdullatif – ALOTHMAN, Basil – KHATTAB, Omar (2023): Blockchain-Based Heuristic Study to Secure UAVs from GPS Spoofing Signals and External Attacks. In 2023 Fifth International Conference on Blockchain Computing and Applications (BCCA). Kuwait, Kuwait, 377–379. Online: https://doi.org/10.1109/BCCA58897.2023.10338915

BASAN, Elena et al. (2021): A Self-Diagnosis Method for Detecting UAV Cyber Attacks Based on Analysis of Parameter Changes. Sensors, 21(2), 509. Online: https://doi.org/10.3390/s21020509

BHARGAVI, M. S. et al. (2025): Unlocking the Potential of Machine Learning for Enhanced Security in Drone-Enabled IoT Networks. In HASSAN, Jahan – KHALIFA, Sara – MISRA, Prasant (szerk.): Machine Learning for Drone-Enabled IoT Networks. Cham: Springer, 107–120. Online: https://doi.org/10.1007/978-3-031-80961-3_6

BURNS, Jaron et al. (2023): On Effectiveness of Machine and Deep Learning Algorithms for Detection of GPS Spoofing Attacks on Unmanned Aerial Vehicles. In 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE). Las Vegas, NV, USA, 2405–2410. Online: https://doi.org/10.1109/CSCE60160.2023.00389

BUSKE, Ivo et al. (2022): Smart GPS Spoofing to Countermeasure Autonomously Approaching Agile Micro UAVs. In High-Power Lasers and Technologies for Optical Countermeasures. 62–67. Online: https://doi.org/10.1117/12.2636236

CONNOR, Roger (2018): The Predator, a Drone That Transformed Military Combat. National Air and Space Museum, 2018. március 9. Online: https://airandspace.si.edu/stories/editorial/predator-drone-transformed-military-combat

DAMIS, Haitham Abu et al. (2026): Emerging Cyber-Security Techniques for Unmanned Aerial Vehicles. In FACHKHA, Claude – FUNG, Benjamin C. M. – TCHAKOUNTÉ, Franklin (szerk.): Safe, Secure, Ethical, Responsible Technologies and Emerging Applications. Cham: Springer, 22–40. Online: https://doi.org/10.1007/978-3-032-05832-4_2

DAVIDOVICH, Barak – NASSI, Ben – ELOVICI, Yuval (2022): Towards the Detection of GPS Spoofing Attacks Against Drones by Analyzing Camera’s Video Stream. Sensors, 22(7), 2608. Online: https://doi.org/10.3390/s22072608

DEVKOTA, Bhawana Poudel – DEVKOTA, Bidur – KANDEL, Laxima Niure (2025): Leveraging Tree-Based Machine Learning and Explainable AI for UAV GPS Spoofing Detection. In SoutheastCon 2025. Concord, NC, USA, 340–345. Online: https://doi.org/10.1109/SoutheastCon56624.2025.10971719

ELDOSOUKY, AbdelRahman – FERDOWSI, Aidin – SAAD, Walid (2020): Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing. IEEE Internet of Things Journal, 7(4), 2840–2854. Online: https://doi.org/10.1109/JIOT.2019.2963337

FADHELI, Hadjer – KHALID, Shamsul Kamal Ahmad – FADZIL, Lokman Mohd (2025): A Non-GPS Return to Home Algorithm for Drones Using Convolutional Neural Network. Journal of Advanced Research in Applied Sciences and Engineering Technology, 51(1), 15–27. Online: https://doi.org/10.37934/araset.51.1.1527

HAKEEM, Abeer et al. (2025): Integrating Artificial Intelligence for Improved Security of IoT-Drones Through Cyber-Physical Attack Detection. Frontiers in Computer Science, 7, 1545282. Online: https://doi.org/10.3389/fcomp.2025.1545282

HOSSAIN, Shaikat – ISLAM, Aminul (2025): Dual-Band Adaptive GPS Antennas for Real-Time Spoofing Mitigation. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). Chittagong, Bangladesh, 1–5. Online: https://doi.org/10.1109/ECCE64574.2025.11013036

INÁNCSI, Mátyás (2022): Cybersecurity Challenges of the Civilian Unmanned Aircraft Systems. Hadmérnök, 17(2), 205–216. Online: https://doi.org/10.32567/hm.2022.2.14

JAIN, Meenal – JAIN, Akshat – SAHOO, Anita (2024): Protective System for Drones Against GPS Spoofing Using Image and Signal Analysis. In Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing (IC3-2024). New York: Association for Computing Machinery, 384–389. Online: https://doi.org/10.1145/3675888.3676075

JAVED, Muhammad Danyal et al. (2025): Cyber Security Framework for AI-Enabled Robotics and Drone Systems. In Advancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses. Hershey: IGI Global Scientific Publishing, 231–262. Online: https://doi.org/10.4018/979-8-3373-0583-7.ch009

KISS Beatrix – PALIK Mátyás (2023): A drónok katonai alkalmazása modern katonai műveletek során. Repüléstudományi Közlemények, 35(1), 115–130. Online: https://doi.org/10.32560/rk.2023.1.9

KRASZNAY Csaba – INÁNCSI Mátyás (2021): Az UAV-ok kiberbiztonsági elemzésének és tanúsításának lehetőségei. Felderítő Szemle, 20(3), 63–79.

LARAIB, Areeba – SIAL, Areesha – UJJAN, Raja Majid Ali (2024): Addresses the Security Issues and Safety in Cyber-Physical Systems of Drones. In SHAH, Imdad Ali – JHANJHI, Noor Zaman (szerk.): Cybersecurity Issues and Challenges in the Drone Industry. Hershey: IGI Global Scientific Publishing, 381–404. Online: https://doi.org/10.4018/979-8-3693-0774-8.ch016

LEE, Ho-Sung – KIM, Jong-Bae (2019): Analysis of Cyber Attack Using Drones and Its Countermeasure Strategy. Journal of Advanced Research in Dynamical and Control Systems, 11(5), 281–289.

LIANG, Chen et al. (2019): Detection of GPS Spoofing Attack on Unmanned Aerial Vehicle System. In CHEN, Xiaofeng – HUANG, Xinyi – ZHANG, Jun (szerk.): Machine Learning for Cyber Security. Cham: Springer, 123–139. Online: https://doi.org/10.1007/978-3-030-30619-9_10

LIANG, Chen et al. (2022): Detection of Global Positioning System Spoofing Attack on Unmanned Aerial Vehicle System. Concurrency and Computation: Practice and Experience, 34(7), e5925. Online: https://doi.org/10.1002/cpe.5925

MAQBOOL, Albia et al. (2024): Proactive Cyber Defense and Forensic Investigation Techniques for Drone Operation: A Holistic Approach. Journal of Theoretical and Applied Information Technology, 102(18), 6697–6709.

MENG, Lianxiao et al. (2021): An Approach of Linear Regression-Based UAV GPS Spoofing Detection. Wireless Communications and Mobile Computing, (1), 5517500. Online: https://doi.org/10.1155/2021/5517500

MOOSAVI, Samin – MOORE, Isabel – GOPALSWAMY, Swaminathan (2024): Using a Sensor-Health-Aware Resilient Fusion for Localization in the Presence of GPS Spoofing Attacks. In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). London, United Kingdom, 498–505. Online: https://doi.org/10.1109/CSR61664.2024.10679455

MYKYTYN, Pavlo et al. (2023): GPS-Spoofing Attack Detection Mechanism for UAV Swarms. In 2023 12th Mediterranean Conference on Embedded Computing (MECO). Budva, Montenegro, 1–8. Online: https://doi.org/10.1109/MECO58584.2023.10154998

MYNUDDIN, Mohammed – KHAN, Sultan Uddin – MAHMOUD, Mahmoud Nabil (2023): Trojan Triggers for Poisoning Unmanned Aerial Vehicles Navigation: A Deep Learning Approach. 2023 IEEE International Conference on Cyber Security Resilience (CSR). Venice, Italy, 432–439. Online: https://doi.org/10.1109/CSR57506.2023.10224932

MYNUDDIN, Mohammed et al. (2024): Trojan Attack and Defense for Deep Learning-Based Navigation Systems of Unmanned Aerial Vehicles. IEEE Access, 12, 89887–89907. Online: https://doi.org/10.1109/ACCESS.2024.3419800

NAIR, Aiswarya S. – THAMPI, Sabu M. – P., Jithu Vijay V. (2025): SoCoMNNet: A SocioCognitive and Memristive Neural Network-Based Context-Aware GPS Spoofing Detection and Mitigation in the Internet of Drones. Vehicular Communications, 56, 100980. Online: https://doi.org/10.1016/j.vehcom.2025.100980

NASCIMENTO AQUILAR PEY, Jeferson – DANIEL AMVAME NZE, Georges – OLIVEIRA ALBUQUERQUE, Robson de (2022): Analysis of Jamming and Spoofing Cyber-Attacks on Drones. In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI). Madrid, Spain, 1–4. Online: https://doi.org/10.23919/CISTI54924.2022.9820201

ORACEVIC, Alma – SALMAN, Ahmad (2024): Unmanned Aerial Vehicles in Peril: Investigating and Addressing Cyber Threats to UAVs. In 2024 International Conference on Smart Applications, Communications and Networking, SmartNets. Harrisonburg, VA, USA, 1–7. Online: https://doi.org/10.1109/SmartNets61466.2024.10577710

ORYE, Erwin et al. (2024): Enhancing the Cyber Resilience of Sea Drones. In 2024 16th International Conference on Cyber Conflict: Over the Horizon (CyCon). Tallinn, Estonia, 83–102. Online: https://doi.org/10.23919/CyCon62501.2024.10685581

POORNIMA, B. – KUMARI, Lalitha Surya (2025): Mitigating GPS Spoofing in AVS: SHA and RSA Algorithms With Proteus Simulation for Real-Time Detection. International Journal of System Assurance Engineering and Management, 16(10), 3375–3389. Online: https://doi.org/10.1007/s13198-025-02864-8

PRASANNA SRINIVASAN, S. – SATHYADEVAN, Shiju (2023): GPS Spoofing Detection in UAV Using Motion Processing Unit. In 2023 11th International Symposium on Digital Forensics and Security (ISDFS). Chattanooga, TN, USA, 1–4. Online: https://doi.org/10.1109/ISDFS58141.2023.10131729

RANGANATHAN, Aanjhan – ÓLAFSDÓTTIR, Hildur – CAPKUN, Srdjan (2016): SPREE: A Spoofing Resistant GPS Receiver. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. New York: Association for Computing Machinery, 348–360. Online: https://doi.org/10.1145/2973750.2973753

RENU, Yuvaraj – SARVESHWARAN, Velliangiri (2025): A Review of Cyber Security Challenges and Solutions in Unmanned Aerial Vehicles (UAVs). Inteligencia Artificial, 28(75), 199–219. Online: https://doi.org/10.4114/intartif.vol28iss75pp199-219

SARKAR, Arupa et al. (2024): Smart Verification of Unmanned Aerial Vehicle GPS Geolocation via Received Signal Strength Indicators. In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). Washington, DC, USA, 1–6. Online: https://doi.org/10.1109/VTC2024-Fall63153.2024.10757631

SATHIYANATHN, S. et al. (2024): Blockchain Integrated Framework to Detect and Prevent CAV Location Spoofing Attack Using GPS Time Series Data Learning and Quantum Cryptography. In 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA). Namakkal, India, 1–7. Online: https://doi.org/10.1109/AIMLA59606.2024.10531539

SHAFIK, Wasswa – MOJTABA MATINKHAH, S. – SHOKOOR, Fawad (2023): Cybersecurity in Unmanned Aerial Vehicles: A Review. International Journal on Smart Sensing and Intelligent Systems, 16(1), 1–16. Online: https://doi.org/10.2478/ijssis-2023-0012

SHAFIQUE, Arslan – MEHMOOD, Abid – ELHADEF, Mourad (2021): Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models. IEEE Access, 9, 93803–93815. Online: https://doi.org/10.1109/ACCESS.2021.3089847

SHAFIQUE, Arslan et al. (2026): Protecting Autonomous Systems From GPS Spoofing With a Machine Learning-Driven Approach. Ad Hoc Networks, 183, 104101. Online: https://doi.org/10.1016/j.adhoc.2025.104101

SHARIAT, Mahyar et al. (2025): UWB-Based Positioning Is Not Invulnerable From Spoofing Attacks: A Case Study of Crazyswarm. Engineering Proceedings, 88(1), 43. Online: https://doi.org/10.3390/engproc2025088043

SINGH, Ratan – CHANDRA MOHAN, B – GOLDA JEYASHEELI, P. (2023): Recent Research and Implementation on Various GPS Spoofing Attacks and Prevention. In 2023 6th International Conference on Recent Trends in Advance Computing (ICRTAC). Chennai, India, 509–515. Online: https://doi.org/10.1109/ICRTAC59277.2023.10480748

SISSODIA, Rajeshwari et al. (2025): Introduction to Artificial Intelligence (AI) and Cybersecurity in Robotics and Drone Systems. In KUMAR, Rajeev et al. (szerk.): Advancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses. Hershey: IGI Global Scientific Publishing, 345–374. Online: https://doi.org/10.4018/979-8-3373-0583-7.ch013

SREE LAKSHMI, P. et al. (2025): Securing the Future of Transportation: A Deep Dive Into Cybersecurity for Autonomous Vehicles. In BHATEJA, Vikrant – DEY, Maitreyee – SIMIC, Milan (szerk.): Intelligent Computing and Automation. Singapore: Springer, 275–285. Online: https://doi.org/10.1007/978-981-96-0143-1_22

TIWARI, Vinita et al. (2023): Mitigation of Jamming and Spoofing Attack on GNSS Signals Using Subspace Projection. In 2023 3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET). Patna, India, 1–6. Online: https://doi.org/10.1109/ICEFEET59656.2023.10452226

TUCKER, Rajanbir Singh – NADEEM, Muhammad – PERVEZ, Shahbaz (2025): Real-Time Detection and Mitigation of GPS Spoofing in UAV Systems. In 2025 12th International Conference on Information Technology (ICIT). Amman, Jordan, 154–160. Online: https://doi.org/10.1109/ICIT64950.2025.11049153

WANG, Zhuang – LI, Guoqiang – WANG, Zhenpo (2025): GPS Attack Detection and Defense for Secure Localization of Automated Vehicles Based on Vehicle-to-Vehicle Technology. IEEE Internet of Things Journal, 12(7), 7723–7735. Online: https://doi.org/10.1109/JIOT.2024.3424518

WEI, Xiaomin et al. (2022): ConstDet: Control Semantics-Based Detection for GPS Spoofing Attacks on UAVs. Remote Sensing, 14(21), 5587. Online: https://doi.org/10.3390/rs14215587

WEI, Xiaomin – WANG, Yao – SUN, Cong (2022): PerDet: Machine-Learning-Based UAV GPS Spoofing Detection Using Perception Data. Remote Sensing, 14(19), 4925. Online: https://doi.org/10.3390/rs14194925

YANG, Ming – ORACEVIC, Alma – DILEK, Selma (2025): Deep Learning-Based Defense Against GPS Spoofing Attacks in Unmanned Aerial Vehicles. In 2025 International Conference on Smart Applications, Communications and Networking, (SmartNets). Istanbul, Turkiye, 1–8. Online: https://doi.org/10.1109/SmartNets65254.2025.11106869

Letöltések

Letölthető adat még nem áll rendelkezésre.