Reinforcement Learning for IoT Security: A Comprehensive Survey

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A Systematic Study on Reinforcement Learning Based Applications

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A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

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Host-based IDS: A review and open issues of an anomaly detection system in IoT

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A Comprehensive Survey on Imputation of Missing Data in Internet of Things

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Security in internet of things: a review on approaches based on blockchain, machine learning, cryptography, and quantum computing

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On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things

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Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

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MalBoT-DRL: Malware Botnet Detection Using Deep Reinforcement Learning in IoT Networks

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Deep Reinforcement Learning in the Advanced Cybersecurity Threat Detection and Protection

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AI-Powered IoT: A Survey on Integrating Artificial Intelligence With IoT for Enhanced Security, Efficiency, and Smart Applications

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Internet of robotic things for mobile robots: Concepts, technologies, challenges, applications, and future directions

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Reinforcement Learning for feedback-enabled cyber resilience

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A Survey on Neural Network Hardware Accelerators

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Combining Edge Computing-Assisted Internet of Things Security with Artificial Intelligence: Applications, Challenges, and Opportunities

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Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review

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Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber–Physical Systems: A Comprehensive Survey

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Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems

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Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, Its Applications, and Future Research Trajectories

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Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives

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Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling

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Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues

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Achieving efficient energy-aware security in IoT networks: a survey of recent solutions and research challenges

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How can applications of blockchain and artificial intelligence improve performance of Internet of Things? – A survey

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Neurosymbolic Reinforcement Learning and Planning: A Survey

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Context-Aware Multiagent Broad Reinforcement Learning for Mixed Pedestrian-Vehicle Adaptive Traffic Light Control

Zhu, R., Wu, S., Li, L., Lv, P., & Xu, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3167029 · Cited by 56

Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security

Hashmi, E., Yamin, M. M., & Yayilgan, S. Y. (2024) · AI and Ethics · doi:10.1007/s43681-024-00529-z · Cited by 68

A Cross-Layer Survey on Secure and Low-Latency Communications in Next-Generation IoT

Martalò, M., Pettorru, G., & Atzori, L. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3390543 · Cited by 51

NG-ICPS: Next Generation Industrial-CPS, Security Threats in the Era of Artificial Intelligence, and Open Challenges With Future Research Directions

Adil, M., Farouk, A., Abulkasim, H., Ali, A., Song, H. H., & Jin, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3486659 · Cited by 48

An Intelligent Digital Twin Method Based on Spatio-Temporal Feature Fusion for IoT Attack Behavior Identification

Wang, H., Di, X., Wang, Y., Ren, B., Gao, G., & Deng, J. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310091 · Cited by 43

Study of Machine Learning for Cloud Assisted IoT Security as a Service

Alsharif, M., & Rawat, D. B. (2021) · Sensors · doi:10.3390/s21041034 · Cited by 39

Automated architectural spatial composition via multi-agent deep reinforcement learning for building renovation

Zhang, Z., Guo, Z., Zheng, H., Li, Z., & Yuan, P. F. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105702 · Cited by 35

Dual Network Computation Offloading Based on DRL for Satellite-Terrestrial Integrated Networks

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New challenges in reinforcement learning: a survey of security and privacy

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Adversarial learning techniques for security and privacy preservation: A comprehensive review

Hathaliya, J. J., Tanwar, S., & Sharma, P. (2022) · Security and Privacy · doi:10.1002/spy2.209 · Cited by 30

Privacy-preserving security of IoT networks: A comparative analysis of methods and applications

Wakili, A., & Bakkali, S. (2025) · Cyber Security and Applications · doi:10.1016/j.csa.2025.100084 · Cited by 30

Exploring the Confluence of IoT and Metaverse: Future Opportunities and Challenges

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Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments: Theoretical Perspective and Challenges

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Resiliency Metrics for Monitoring and Analysis of Cyber-Power Distribution System With IoTs

Sarker, P. S., Sadanandan, S. K., & Srivastava, A. K. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3183180 · Cited by 30

Multi-type task offloading for wireless Internet of Things by federated deep reinforcement learning

Tong, Z., Wang, J., Mei, J., Li, K., Li, W., & Li, K. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.04.004 · Cited by 29

Learning Near-Optimal Intrusion Responses Against Dynamic Attackers

Hammar, K., & Stadler, R. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3293413 · Cited by 38

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

Harahsheh, K., & Chen, C. (2023) · Informatica · doi:10.31449/inf.v47i6.4635 · Cited by 38

A Comprehensive Analysis of Machine Learning and Deep Learning Approaches towards IoT Security

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Navigating IoT Security: Insights into Architecture, Key Security Features, Attacks, Current Challenges and AI-Driven Solutions Shaping the Future of Connectivity

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Distributed Cyber-infrastructures and Artificial Intelligence in Hybrid Post-Quantum Era

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Multi-Agent Reinforcement Learning for Cybersecurity: Classification and survey

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Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

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Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive SurveyPreprint

Abdellatif, A. A., Mhaisen, N., Chkirbene, Z., Mohamed, A. M. S., Erbad, A., & Guizani, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.04087 · Cited by 39

Optimizing Intrusion Detection in IoMT Networks Through Interpretable and Cost-Aware Machine Learning

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Intelligent defense strategies: Comprehensive attack detection in VANET with deep reinforcement learning

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A novel reinforcement learning-based hybrid intrusion detection system on fog-to-cloud computing

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Reinforcement Learning-Based Device Scheduling for Renewable Energy-Powered Federated Learning

Cui, Y., Cao, K., & Wei, T. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3210008 · Cited by 21

Deep Semantic Communication for Knowledge Sharing in Internet of Vehicles

Wang, Z., Leng, S., Zhang, H., & Yuen, C. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3531910 · Cited by 24

Asynchronous Advantage Actor-Critic (A3C) Learning for Cognitive Network Security

Muhati, E., & Rawat, D. B. (2021) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tpsisa52974.2021.00012 · Cited by 24

From Smart Devices to Smarter Systems: The Evolution of Artificial Intelligence of Things (AIoT) with Characteristics, Architecture, Use Cases and Challenges

Parihar, V., Malik, A., Bhawna, Bhushan, B., & Chaganti, R. (2023) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-31952-5_1 · Cited by 20

A Comprehensive Empirical Analysis of Data Sets, Regression-Based Feature Selectors, and Linear SVM Classifiers for Intrusion Detection Systems

Azimjonov, J., & Kim, T. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3415499 · Cited by 19

A Deep Deterministic Policy Gradient Learning Approach to Missile Autopilot Design

Candeli, A., Tommasi, G. D., Lui, D. G., Mele, A., Santini, S., & Tartaglione, G. (2022) · IEEE Access · doi:10.1109/access.2022.3150926 · Cited by 20

Detection of DDoS attack in IoT traffic using ensemble machine learning techniques

Pandey, N., & Mishra, P. K. (2023) · Networks and Heterogeneous Media · doi:10.3934/nhm.2023061 · Cited by 19

Security defense strategy algorithm for Internet of Things based on deep reinforcement learning

Feng, X., Han, J., Zhang, R., Xu, S., & Xia, H. (2023) · High-Confidence Computing · doi:10.1016/j.hcc.2023.100167 · Cited by 19

Reinforcement Learning Applications in Cyber Security: A Review

CENGİZ, E., & Gök, M. (2023) · Sakarya University Journal of Science · doi:10.16984/saufenbilder.1237742 · Cited by 25

Multiagent Cooperative Caching Policy in Industrial Internet of Things

Zhen, Y., Chen, W., Zheng, L., Li, X., & Mu, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3164447 · Cited by 17

Deep Reinforcement Learning Approach for Cyberattack Detection

Tareq, I., Elbagoury, B. M., El-Regaily, S. A., & El-Horbaty, E.-S. M. (2024) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v20i05.48229 · Cited by 19

Scalable Intrusion Detection in IoT Networks Via Property Testing and Federated Edge AI

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An evolutionary computation-based machine learning for network attack detection in big data traffic

Wang, Y., Zhang, H., Wei, Y., Wang, H., Peng, Y. L., Bin, Z., & Li, W. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110184 · Cited by 16

Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness

Giacomoni, L., & Parisis, G. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621288 · Cited by 18

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Sonavane, S. M., Prashantha, G. R., Nikam, P. D., Mayuri, A. V. R., Chauhan, J., Sountharrajan, S., & Bavirisetti, D. P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24224 · Cited by 16

Deep Reinforcement Learning based Intrusion Detection System with Feature Selections Method and Optimal Hyper-parameter in IoT Environment

Bakhshad, S., Ponnusamy, V., Annur, R., Waqasyz, M., Alasmary, H., & Tux, S. (2022) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits55221.2022.9832976 · Cited by 14

DNS for IoT: A Survey

Ayoub, I., Balakrichenan, S., Khawam, K., & Ampeau, B. (2023) · Sensors · doi:10.3390/s23094473 · Cited by 15

Recent Reinforcement Learning and Blockchain Based Security Solutions for Internet of Things: Survey

Gasmi, R., Hammoudi, S., Lamri, M., & Harous, S. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10664-1 · Cited by 14

A Survey on Verification of Security and Safety in IoT Systems

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Secure State Estimation of Cyber-Physical System under Cyber Attacks: Q-Learning vs. SARSA

Jin, Z., Ma, M., Zhang, S., Hu, Y., Zhang, Y., & Sun, C. (2022) · Electronics · doi:10.3390/electronics11193161 · Cited by 14

Machine learning approaches for active queue management: A survey, taxonomy, and future directions

Toopchinezhad, M. P., & Ahmadi, M. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111174 · Cited by 15

An Optimal Active Defensive Security Framework for the Container-Based Cloud with Deep Reinforcement Learning

Li, Y., Hu, H., Liu, W., & Yang, X. (2023) · Electronics · doi:10.3390/electronics12071598 · Cited by 14

Heterogeneous IoT (HetIoT) security: techniques, challenges and open issues

Mahadik, S. S., Pawar, P. M., & Muthalagu, R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16715-w · Cited by 13

Smart Jamming for Secrecy: Deep Reinforcement Learning Enabled Secure Visible Light Communication

Liu, S., Liu, X., Du, X., & Guizani, M. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3458046 · Cited by 12

Self-adaptive cyber defense for sustainable IoT: A DRL-based IDS optimizing security and energy efficiency

Jamshidi, S., Amirnia, A., Nikanjam, A., Nafi, K. W., Khomh, F., & Keivanpour, S. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104176 · Cited by 13

A Systematic Review of Machine Learning and Deep Learning Techniques for DDoS Attack Mitigation in IoT and Edge Computing Systems

Owusu, E., Danlard, I., Opoku, G., Dede, A., Klogo, G. S., Boateng, K. O., & Akowuah, E. K. (2025) · Security and Privacy · doi:10.1002/spy2.70147 · Cited by 12

Machine Learning Approaches in Blockchain Technology-Based IoT Security: An Investigation on Current Developments and Open Challenges

Hemashree, P., Kavitha, V., Mahalakshmi, S. B., Praveena, K., & Tarunika, R. (2024) · Signals and communication technology · doi:10.1007/978-3-031-49593-9_7 · Cited by 12

Feasibility discussion of quantum cryptography for Internet of Things security: a literature review

Yang, Y., Lin, Y., Xiao, J., & Zhong, Z. (2025) · Optical and Quantum Electronics · doi:10.1007/s11082-025-08168-2 · Cited by 13

A comprehensive review of cybersecurity vulnerabilities, threats, and solutions for the Internet of Things at the network-cum-application layer

Razaque, A., Hariri, S., Alajlan, A. M., & Yoo, J. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100789 · Cited by 19

The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning

Rigaki, M., & García, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51482-1_3 · Cited by 10

Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions

Fan, J., Shar, L. K., Zhang, R., Liu, Z., Yang, W., Niyato, D. T., & Lam, K.-Y. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3653434 · Cited by 33

TSGS: Two-stage security game solution based on deep reinforcement learning for Internet of Things

Feng, X., Xia, H., Xu, S., Xu, L., & Zhang, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120965 · Cited by 10

Towards Secure IoMT: Attack Detection Using Deep Q-Learning in Healthcare Networks

Daher, L. A. (2023) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese60595.2023.10468942 · Cited by 9

Data-Based Q-Learning for Replay Attack Detection in Cyber-Physical Systems

Qin, J., Zhang, Z., & Zhao, H. (2025) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2025.3532881 · Cited by 9

Prediction and Detection of Cyberattacks using AI Model in Virtualized Wireless Networks

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RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols

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Reinforcement Learning in action: Powering intelligent intrusion responses to advanced cyber threats in realistic scenarios

Iturbe, E., Rego, A., Llorente-Vazquez, O., Rios, E., Dalamagkas, C., Merkouris, D., & Toledo, N. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129168 · Cited by 13

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation

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A Taxonomy of AI Techniques for Security and Privacy in Cyber–Physical Systems

Bandi, A. (2024) · Journal of Computational and Cognitive Engineering · doi:10.47852/bonviewjcce42021539 · Cited by 8

Leveraging AI and Blockchain for Enhanced IoT Cybersecurity

Reshi, I. A., & Sholla, S. (2024) · Blockchain technologies · doi:10.1007/978-981-97-1249-6_14 · Cited by 7

Solution of IoT Security and Privacy Challenges: A Systematic Literature Review

Alsafran, J., Elmedany, W. M., & Sharif, M. S. (2024) · Arab ICT Conference (AICTC) · doi:10.1109/aictc58357.2024.10735034 · Cited by 7

Investigating Artificial Intelligence Approaches to Cybersecurity in Internet of Things Manufacturing Systems and a Deep Hybrid Learning Framework for Malware Detection

Maghanaki, M., Keramati, S., Chen, F. F., & Shahin, M. (2026) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4071232 · Cited by 9

Machine Learning Algorithms and IoT Sensors for Securing the Networks

Sridevi, & Kolhar, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-7758-1.ch009 · Cited by 7

Inter-Multilevel Super-Orthogonal Space–Time Coding Scheme for Reliable ZigBee-Based IoMT Communications

Ma, S., Alkhaleefah, M., Chang, Y.-L., Chuah, J. H., Chang, W., Ku, C.-S., Wu, M.-C., & Chang, L. (2022) · Sensors · doi:10.3390/s22072695 · Cited by 7

AI Makes Crypto Evolve

Zolfaghari, B., & Koshiba, T. (2022) · Applied System Innovation · doi:10.3390/asi5040075 · Cited by 7

Security Challenges and Solutions in IoT

Hajla, S. E., Ennaji, E. M., Maleh, Y., & Mounir, S. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7540-2.ch002 · Cited by 6

An Overview of Q‐Learning and Deep Q‐Learning for an Autonomous Multi‐ UAV Wireless Network

Sharma, V. C., & Roy, S. (2025) · Expert Systems · doi:10.1111/exsy.70143 · Cited by 6

IoT Attacks, Security Concerns, and Reinforcement Learning Solutions: A Comprehensive Survey

Kalantri, R. A., & Bansode, R. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-0165-3_40 · Cited by 2

Deep Reinforcement Learning for Cybersecurity Applications

Mathew, A. (2021) · International Journal of Computer Science and Mobile Computing · doi:10.47760/ijcsmc.2021.v10i12.005 · Cited by 6

Trustworthy Communication Channel for the IoT Sensor Nodes Using Reinforcement Learning

Zaman, S., Iqbal, M. M., Tauqeer, H., Shahzad, M., & Akbar, G. (2022) · International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE) · doi:10.1109/etecte55893.2022.10007382 · Cited by 3

RRIoT: Recurrent reinforcement learning for cyber threat detection on IoT devices

Rookard, C., & Khojandi, A. (2023) · Computers & Security · doi:10.1016/j.cose.2024.103786 · Cited by 32

Recent Reinforcement Learning and Blockchain Based Security Solutions for Internet of Things: Survey

Gasmi, R., Hammoudi, S., Lamri, M., & Harous, S. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10664-1 · Cited by 14