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Adversarial Robust Reinforcement Learning for Secure and Regulation-Aware Internet of Things Systems Cover

Adversarial Robust Reinforcement Learning for Secure and Regulation-Aware Internet of Things Systems

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Open Access
|Jul 2026

References

  1. Xiao L, Wan X, Lu X, Zhang Y, Wu D. “IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?”, IEEE Signal Processing Magazine, 35(5), 41–49, 2018. DOI: https://doi.org/10.1109/MSP.2018.2825478.
  2. Lei L, Tan Y, Zheng K, Liu S, Zhang K, Shen X. “Deep reinforcement learning for autonomous internet of things: Model, applications and challenges”, IEEE Communications Surveys & Tutorials, 22(3): 1722–1760, 2020, DOI: https://doi.org/10.1109/COMST.2020.2988367.
  3. Uprety A, Rawat DB. “Reinforcement learning for iot security: A comprehensive survey”, IEEE Internet of Things Journal. 2020 Nov 26;8(11):8693-706, DOI: https://doi.org/10.1109/JIOT.2020.3040957.
  4. Nguyen TT, Reddi VJ. “Deep reinforcement learning for cyber security”, IEEE Transactions on Neural Networks and Learning Systems, 34(8), 3779–3795, 2021, DOI: https://doi.org/10.1109/TNNLS.2021.3121870
  5. Tooki OO, Popoola OM. “Advances in the Application of Model-Free Reinforcement Learning in Protecting Transactive Energy Systems against Cyberthreats: A Review”. Results in Engineering, 28, 108284, 2025. DOI: https://doi.org/10.1016/j.rineng.2025.108284.
  6. Arazzi M, Nicolazzo S, Nocera A. “A deep reinforcement learning approach for security-aware service acquisition in IoT”. Journal of Information Security and Applications, 85, 103856, 2024. DOI: https://doi.org/10.1016/j.jisa.2024.103856
  7. Srikanth MV, Sunitha P, Kumar AS, Akshaykranth A. “A Novel Framework for Intrusion Detection in IOT Networks using Hybrid Optimization Algorithm and Convolutional Neural Networks”. Franklin Open, 100461, 2025. DOI: https://doi.org/10.1016/j.ftaope.2025.100461.
  8. Mahjoub C, Hamdi M, Alkanhel RI, Mohamed S, Ejbali R. “An adversarial environment reinforcement learning-driven intrusion detection algorithm for Internet of Things”. EURASIP Journal on Wireless Communications and Networking, 2024(1), 21,2024. DOI: https://doi.org/10.1186/s13638-024-02348-6
  9. Xie J. “Application study on the reinforcement learning strategies in the network awareness risk perception and prevention”. International Journal of Computational Intelligence Systems, 17(1), 112, 2024. DOI: https://doi.org/10.1007/s44196-024-00492-x.
  10. Alnfiai MM. “AI-powered cyber resilience: a reinforcement learning approach for automated threat hunting in 5G networks”, EURASIP Journal on Wireless Communications and Networking, 2025(1), 68, 2025. DOI: https://doi.org/10.1186/s13638-025-02497-2.
  11. Kumar A, Singh D. “Adaptive epsilon greedy reinforcement learning method in securing IoT devices in edge computing”, Discover Internet of Things, 4(1), 27, 2024. DOI: https://doi.org/10.1007/s43926-024-00080-7.
  12. Feng X, Han J, Zhang R, Xu S, Xia H. “Security defense strategy algorithm for Internet of Things based on deep reinforcement learning”, High-Confidence Computing, 4(1), 100167, 2024. DOI: https://doi.org/10.1016/j.hcc.2023.100167
  13. Qiu K, Yan M, Luo T, Chen F. “FedAware: a distributed IoT intrusion detection method based on fractal shrinking autoencoder”, Journal of King Saud University Computer and Information Sciences, 37(7), 202, 2025. DOI: https://doi.org/10.1007/s44443-025-00153-9.
  14. Khraisat A, Alazab A, Alazab M, Obeidat A, Singh S, Jan T. “Federated learning for intrusion detection in IoT environments: a privacy-preserving strategy”, Discover Internet of Things, 5(1), 72, 2025. DOI: https://doi.org/10.1007/s43926-025-00169-7
  15. Vadigi S, Sethi K, Mohanty D, Das SP, Bera P. “Federated reinforcement learning based intrusion detection system using dynamic attention mechanism”, Journal of Information Security and Applications, 78:103608, 2023. DOI: https://doi.org/10.1016/j.jisa.2023.103608
  16. Zhang P, Wang C, Jiang C, Han Z. “Deep reinforcement learning assisted federated learning algorithm for data management of IIoT”. IEEE Transactions on Industrial Informatics, 17(12), 8475–8484, 2021. DOI: https://doi.org/10.1109/TII.2021.3064351.
  17. Ouyang Y, Cao E, Liu B. “Blockchain-integrated AI framework for secure IoT-based digital advertising ecosystems”, Discover Internet of Things, 5(1), 151, 2025. DOI: https://doi.org/10.1007/s43926-025-00262-x.
  18. Huang Y, Ma M, Raymond WJ, Chow CO. “An adaptive intrusion detection system for the internet of things using large language models and post-quantum-secure blockchain”, Computer Networks, 274, 111819, 2025. DOI: https://doi.org/10.1016/j.comnet.2025.111819.
  19. Rizzardi A, Sicari S, Porisini AC. “Deep Reinforcement Learning for intrusion detection in Internet of Things: Best practices, lessons learnt, and open challenges”, Computer Networks, 236, 110016, 2023. DOI: https://doi.org/10.1016/j.comnet.2023.110016.
  20. Gueriani A, Kheddar H, Mazari AC. “Deep reinforcement learning for intrusion detection in IoT: A survey”, In 2023 2nd International Conference on Electronics, Energy and Measurement (IC2EM), pp. 1–7, 2023. DOI: https://doi.org/10.1109/IC2EM59347.2023.10419560.
  21. Ren K, Zeng Y, Zhong Y, Sheng B, Zhang Y. “MAFSIDS: a reinforcement learning-based intrusion detection model for multi-agent feature selection networks”, Journal of Big Data, 10(1), 137, 2023. DOI: https://doi.org/10.1186/s40537-023-00814-4.
  22. Javadpour A, Ja'fari F, Benzaïd C, Taleb T. “An optimized reinforcement learning based MTD mutation strategy for securing edge IoT against DDoS attack”, Journal of Information Security and Applications, 93, 104138, 2025. DOI: https://doi.org/10.1016/j.jisa.2025.104138.
  23. Xu X, Hu H, Liu Y, Tan J, Zhang H, Song H. “Moving target defense of routing randomization with deep reinforcement learning against eavesdropping attack”, Digital Communications and Networks, 8(3), 373–387, 2022. DOI: https://doi.org/10.1016/j.dcan.2022.01.003.
  24. Andreou A, Mavromoustakis CX, Markakis E, Bourdena A, Mastorakis G. “Enhancing network slice security with Deep Reinforcement Learning and Moving Target Defense strategies”, Discover Internet of Things, 5(1), 67, 2025. DOI: https://doi.org/10.1007/s43926-025-00161-1
  25. Sun R, Fei J, Zhu Y, Guo Z. “Multi-Agent Reinforcement Learning for Moving Target Defense Temporal Decision-Making Approach Based on Stackelberg-FlipIt Games”, Computers, Materials & Continua, 84(2), 3765, 2025. DOI: https://doi.org/10.32604/cmc.2025.064849.
  26. Ghourab EM, Naser S, Muhaidat S, Bariah L, Al-Qutayri M, Damiani E, Sofotasios PC. “Moving target defense approach for secure relay selection in vehicular networks”, Vehicular Communications, 47:100774, 2024. DOI: https://doi.org/10.1016/j.vehcom.2024.100774.
  27. Ghourab EM, Azab M, Mansour A. “Spatiotemporal diversification by moving-target defense through benign employment of false-data injection for dynamic, secure cognitive radio network”, Journal of Network and Computer Applications, 138, 1–4, 2019. DOI: https://doi.org/10.1016/j.jnca.2019.02.020.
  28. Masud MT, Keshk M, Moustafa N, Turnbull B, Susilo W. “Vulnerability defence using hybrid moving target defence in Internet of Things systems”, Computers & Security, 153,104380, 2025. DOI: https://doi.org/10.1016/j.cose.2025.104380.
  29. Baccour E, Erbad A, Mohamed A, Hamdi M, Guizani M. “Multi-agent reinforcement learning for privacy-aware distributed CNN in heterogeneous IoT surveillance systems”, Journal of Network and Computer Applications, 230, 103933, 2024. DOI: https://doi.org/10.1016/j.jnca.2024.103933.
  30. Kusuma SM, Veena KN, Kumar BV, Naresh E, Marianne LA. “Meta heuristic technique with reinforcement learning for node deployment in wireless sensor networks”, SN Computer Science, 5(5), 554, 2024. DOI: https://doi.org/10.1007/s42979-024-02906-1.
  31. Peng C, Zhang Y, Jiang L. “Integrating IoT data and reinforcement learning for adaptive macroeconomic policy optimization”, Alexandria Engineering Journal, 119, 222–231, 2025. DOI: https://doi.org/10.1016/j.aej.2025.01.065.
  32. Hashemian A, Derakhshanfard N. “Target Tracking in Internet of Things Using Reinforcement Learning”, Array, 28, 100551, 2025. DOI: https://doi.org/10.1016/j.array.2025.100551.
  33. Wang L, Wei Z, Guo W. “Securing IoT Communication Using Physical Sensor Data Graph Layer Security with Federated Multi-agent Deep Reinforcement Learning”, In 2023 8th International Conference on Signal and Image Processing (ICSIP), pp. 860–865, 2023. DOI: https://doi.org/10.1109/ICSIP57908.2023.10271026.
  34. Srikanth MV, Sunitha P, Kumar AS, Akshaykranth A. “A Novel Framework for Intrusion Detection in IOT Networks using Hybrid Optimization Algorithm and Convolutional Neural Networks”, Franklin Open, 14, 100461, 2025. DOI: https://doi.org/10.1016/j.fraope.2025.100461.
  35. Rahman MM, Al Shakil S, Mustakim MR. “A survey on intrusion detection system in IoT networks”, Cyber Security and Applications, 3, 100082, 2025. DOI: https://doi.org/10.1016/j.csa.2024.100082
  36. Tan J, Jin H, Zhang H, Zhang Y, Chang D, Liu X, Zhang H. “A survey: When moving target defense meets game theory”, Computer Science Review, 48,100544, 2023. DOI: https://doi.org/10.1016/j.cosrev.2023.100544.
  37. Arazzi M, Nicolazzo S, Nocera A. “A deep reinforcement learning approach for security-aware service acquisition in IoT”, Journal of Information Security and Applications, 85, 103856, 2024. DOI: https://doi.org/10.1016/j.jisa.2024.103856
  38. Kumar A, Singh D. “Securing IoT devices in edge computing through reinforcement learning”, Computers & Security, 155, 104474, 2025. DOI: https://doi.org/10.1016/j.cose.2025.104474
  39. Rahman MM, Al Shakil S, Mustakim MR. “A survey on intrusion detection system in IoT networks”, Cyber Security and Applications, 3, 100082, 2025. DOI: https://doi.org/10.1016/j.csa.2024.100082
  40. Tan J, Jin H, Zhang H, Zhang Y, Chang D, Liu X, Zhang H. “A survey: When moving target defense meets game theory”, Computer Science Review, 48, 100544, 2023. DOI: https://doi.org/10.1016/j.cosrev.2023.100544
  41. Zuo P, Miao C, Fu C, Wang X, Liu X, Liu B. SMAPPO: A security-aware multi-agent reinforcement learning framework for secure computation offloading in SAGIN. Journal of King Saud University Computer and Information Sciences. 2025 Dec;37(10):336, DOI: https://doi.org/10.1007/s44443-025-00315-9.
  42. Far AZ, Far MZ, Gharibzadeh S, Naeini HK, Amini L, Zangeneh S, Rahimi M, Asadi S. “Artificial intelligence for secured information systems in smart cities: Collaborative iot computing with deep reinforcement learning and blockchain”, arXiv preprint arXiv:2409.16444, 2024. DOI: https://doi.org/10.48550/arXiv.2409.16444.
  43. Hu W, Alzubi JA, Shreyas J, Al-Razgan M, Ali YA, Karthikayan A. “Enhancing IoT Network Security by Anomaly Detection and Intrusion Prevention Using Gannet Optimization-Based Adaptive Deep Capsule Network”, International Journal of Computational Intelligence Systems, 18(1), 237, 2025. DOI: https://doi.org/10.1007/s44196-025-00940-2.
  44. Javadpour A, Ja'fari F, Taleb T, Shojafar M, Benzaïd C. “A comprehensive survey on cyber deception techniques to improve honeypot performance”, Computers & Security, 140, 103792, 2024. DOI: https://doi.org/10.1016/j.cose.2024.103792
  45. Javadpour A, Ja’fari F, Benzaïd C, Taleb T. “An optimized reinforcement learning based MTD mutation strategy for securing edge IoT against DDoS attack”, Journal of Information Security and Applications, 93, 104138, 2025. DOI: https://doi.org/10.1016/j.jisa.2025.104138
  46. Aparcana-Tasayco AJ, Deng X, Park JH. “A systematic review of anomaly detection in IoT security: towards quantum machine learning approach”, EPJ Quantum Technology, 12(1), 1–39, 2025. DOI: https://doi.org/10.1140/epjqt/s40507-025-00414-6.
DOI: https://doi.org/10.2478/ias-2026-0005 | Journal eISSN: 1554-1029 | Journal ISSN: 1554-1010
Language: English
Page range: 89 - 106
Published on: Jul 8, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2026 Mohammed Farsi, Elsayed Atlam, published by Cerebration Science Publishing Co., Limited
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