Reinforcement Learning for IoT Security: A Comprehensive Survey

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

The Internet of Things: A survey

Atzori, L., Iera, A., & Morabito, G. (2010) · Computer Networks · doi:10.1016/j.comnet.2010.05.010 · Cited by 15,613

Mastering the game of Go with deep neural networks and tree search

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612

Continuous control with deep reinforcement learning

Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782

A neural substrate of prediction and reward

Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388

Reinforcement Learning: An Introduction

Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825

Asynchronous Methods for Deep Reinforcement Learning

Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1602.01783 · Cited by 7,132

Finite-time Analysis of the Multiarmed Bandit Problem

Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002) · Machine Learning · doi:10.1023/a:1013689704352 · Cited by 6,033

Beyond Reinforcement Learning for network security: A comprehensive survey and tutorial

Javadpour, A., Ja’fari, F., Taleb, T., Türkmen, F., & Benzaïd, C. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104294 · Cited by 6

Integration of emerging technologies in cybersecurity for healthcare: A systematic review

Patra, D., & Rajagopalan, N. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104763 · Cited by 6

Access management based on deep reinforcement learning for effective cloud storage security

Venkataswamy, S. B., Patil, K. S., Narayanaswamy, H. K., & Veerabadrappa, K. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02596-1 · Cited by 6

Machine Learning Enabled Intrusion Detection for Edge Devices in the Internet of Things

Alsharif, M., & Rawat, D. B. (2023) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc57344.2023.10099276 · Cited by 6

A Futuristic Survey on Learning Techniques for Internet of Things (IoT) Security : Developments, Applications, and ChallengesPreprint

Patel, C., Vyas, S., Saikia, P., kalariya, D., & parmar, N. (2022) · doi:10.36227/techrxiv.19642977.v1 · Cited by 6

A Non-Markovian Game Approach on Labeled Attack Graphs for Security Decision-Making in Industrial Control Systems

Yue, Y., Tan, S., Tao, Y., Liu, N., & Lü, J. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3616599 · Cited by 5

A trust-aware and cost-optimized blockchain oracle selection model with deep reinforcement learning

Zhang, H. C., Li, S., Bao, H., Cao, Y., & Li, J. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108489 · Cited by 5

A Paradigm Shift in IoT Cyber-Security: A Systematic Review

Verma, P., Sharma, R., & Mistry, M. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.47129 · Cited by 5

AutoCAT: Reinforcement Learning for Automated Exploration of Cache-Timing Attacks

Luo, M., Xiong, W., Lee, G., Li, Y., Yang, X., Zhang, A., Tian, Y., Lee, H.-H. S., & Suh, G. E. (2023) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca56546.2023.10070947 · Cited by 5

Distributed Remote Secure ADP-Based Control for Interconnected Power Systems Under Cyber-Attacks

Zhang, L., Chen, Y., & Abdelhameed, E. H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3377201 · Cited by 5

PRIVOT: Privacy-Resilient Intelligent DAG Blockchain Architecture for IoT

Alanazi, F., Zareei, M., & Arreola, A. R. (2025) · IEEE Access · doi:10.1109/access.2025.3593365 · Cited by 4

Self Healing of a Mixed Autonomy Traffic System Using Reinforcement Learning and Attention

Fatima, S., Ellefsen, K. O., & Moonen, L. (2025) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2025.3606539 · Cited by 4

A multi-agent reinforcement learning with multi-task learning and graph edge attention networks for dynamic flexible job shop scheduling

Zhao, F., & Yu, J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114277 · Cited by 4

A Semantic-Based Approach for Self-Configuring Smart Home Systems Using Feedback-Based Reinforcement Learning

Ran, W., Nantogma, S., Yang, Q., Zhang, S., Wang, Y., & Xu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3540086 · Cited by 4

A Dynamic Reward-Based Deep Reinforcement Learning for IoT Intrusion Detection

Ren, K., Liu, L., Bai, H., & Wen, Y. (2024) · International Conference on Intelligent Communication and Networking (ICN) · doi:10.1109/icn64251.2024.10865958 · Cited by 4

Path optimization model of smart elderly care service based on reinforcement learning and particle swarm optimization

Tang, Y., Fu, P., & Zhang, B. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251337914 · Cited by 4

Enhancing Vehicular Network Security, Privacy, and Trust Through Reinforcement Learning: A Comprehensive Survey

Mianji, E. M., Muntean, G., & Tal, I. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3612202 · Cited by 9

An AI-based Approach for Scalable Cyber-Physical Optimal Response in Power Systems

Sun, S., Hossain‐McKenzie, S., Homoud, L. A., Haque, K. A., Goulart, A., & Davis, K. (2024) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec60005.2024.10472265 · Cited by 4

Distributed Reinforcement Learning for IoT Security in Heterogeneous and Distributed Networks

Jagatheesaperumal, S. K., Rahouti, M., Aledhari, M., Hafid, A., Oliveira, D., Drid, H., & Amin, R. (2024) · Computing&AI Connect · doi:10.69709/caic.2024.100109 · Cited by 5

Research on Image Recognition based on Reinforcement Learning

Luo, J., & Luo, R. (2023) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl58838.2023.10166036 · Cited by 4

Reinforcement Learning Based Active Attack Detection and Blockchain Technique to Protect the Data from the Passive Attack in the Autonomous Mobile Network

Sivasankar, C., & Kumanan, T. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10576-0 · Cited by 4

Optimization under Uncertainty: Machine Learning Approach

Mohammadi, R., & Farsijani, H. (2023) · International Journal of Innovation in Management Economics and Social Sciences · doi:10.59615/ijimes.3.2.23 · Cited by 4

A Heterogenous IoT Attack Detection Through Deep Reinforcement Learning: A Dynamic ML Approach

Baby, R., Pooranian, Z., Shojafar, M., & Tafazolli, R. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278685 · Cited by 4

IoT Security in a Connected World: Analyzing Threats, Vulnerabilities, and Mitigation Strategies

Tawffaq, M. R., Jasim, M. A., Mejbel, B. G., Issa, S. S., Alamro, L., Shulha, V., & Aram, E. (2024) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct64283.2024.10749944 · Cited by 3

A Hybrid Graph Neural Network-Based Reinforcement Learning Approach for Adaptive Cybersecurity Risk Management in FinTech

Radha, M., Vasa, Y., Kumbham, A. R., Vallurupalli, P., Kumar, S. A., & Dhivya, A. B. (2025) · International Conference on Computing Technologies & Data Communication (ICCTDC) · doi:10.1109/icctdc64446.2025.11158732 · Cited by 3

A Comparison of Data Imputation Methods Utilizing Machine Learning for a New IoT System Platform

Kalay, S., Çinar, E. M., & Sarıçiçek, İ. (2022) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit55151.2022.9804113 · Cited by 3

Security and Privacy Threats of IoT Devices: A & Short Review

Veluvarthi, R., Rameswarapu, A., Kalyan, K. V., Piri, J., & Acharya, B. (2023) · doi:10.1109/icspc57692.2023.10125863 · Cited by 3

A tutorial on reinforcement learning in selected aspects of communications and networking

Boryło, P., Biernacka, E., Domżał, J., Kądziołka, B., Kantor, M., Rusek, K., Skała, M., Wajda, K., Wójcik, R., & Ząbek, W. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.05.019 · Cited by 3

Q-learning based strategy analysis of cyber-physical systems considering unequal cost

Chen, X., Cheng, J., Jiang, L., Li, Q., Wang, T., & Li, D. (2023) · Intelligent and Converged Networks · doi:10.23919/icn.2023.0012 · Cited by 3

Machine Learning Approach to the Internet of Things Threat Detection

Upadhyay, A., Naaz, S., Thakur, V., & Ansari, I. R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-6755-1_31 · Cited by 3

Trust Metric-Based Anomaly Detection via Deep Deterministic Policy Gradient Reinforcement Learning Framework

Shruthi, N. K., & Siddesh, G. K. (2023) · International journal of Computer Networks & Communications · doi:10.5121/ijcnc.2023.15601 · Cited by 4

Reinforcement-Learning-Based Policy Design for Outage Minimization in DF Relaying Networks

Geng, Y., Liu, E., Wang, R., Sun, P., Lu, B., & Wang, J. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3347279 · Cited by 3

Adaptive IoT Defense With Deep Q-Learning Model for DNS Spoofing Prevention in Self-Organizing Networks

Naseer, M., Ullah, F., Ahmad, J. E., Jhaveri, R. H., & Gadekallu, T. R. (2025) · IEEE Communications Standards Magazine · doi:10.1109/mcomstd.2025.3575510 · Cited by 3

Journal of Cybersecurity and Privacy: A New Open Access Journal

Rawat, D. B. (2021) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp1010010 · Cited by 3

Investigation of Energy Optimization for Spectrum Sensing in Distributed Cooperative IoT Network Using Deep Learning Techniques

Pavithra, M., Rajmohan, R., Kumar, T. A., Usharani, S., & Bala, P. M. (2022) · doi:10.1002/9781119821878.ch6 · Cited by 3

Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks

Ghatak, G. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3305519 · Cited by 2

Deep Reinforcement Learning-Based Feature Enhancement Method for UAV-Enabled River Surface Flow Velocity Measurement

Zhang, M., Xu, M., & Li, X. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3633273 · Cited by 2

Artificial Intelligence in Mitigating Security Threats for Lightweight IoT Devices: A Survey of Technologies, Protocols, and Future Challenges

Ouyang, N., Shatte, A. B. R., Lu, Z., Chen, C., & Xiang, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3649316 · Cited by 3

Resource Allocation for Blockchain-Enabled Fog Computing with Deep Reinforcement Learning

Zhang, Y., Tu, S., Waqas, M. M., Yang, Y., Wu, A., & Bai, X. (2022) · International Conference on Communication and Network Security (ICCNS) · doi:10.1145/3586102.3586134 · Cited by 2

Collaborative path penetration in 5G-IoT networks: A multi-agent deep reinforcement learning approach

Shen, H., Li, X. D., Wang, Y., Wang, T., & Bai, G. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01939-4 · Cited by 4

Challenges and Solutions for Countering Emerging Security Threats to IoT Devices

Tiwari, N., Pandey, A. K., & Kokala, A. (2025) · International Journal of experimental research and review · doi:10.52756/ijerr.2025.v47.005 · Cited by 2

DNS Traffic Monitoring to Access Vulnerability in the Internet of Healthcare Things Networks: A Survey

Weqar, M., Mehfuz, S., & Gupta, D. (2023) · International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON) · doi:10.1109/reedcon57544.2023.10150996 · Cited by 2

Challenges, Opportunities, and Future Prospects

Abdel‐Basset, M., Moustafa, N., Hawash, H., & Ding, W. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-89025-4_10 · Cited by 2

Recent Advances in Artificial Intelligence Enabled Tutoring Systems: A Survey

Yesir, I., & Rawat, D. B. (2023) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc57344.2023.10099098 · Cited by 2

A Threat-Aware and Efficient Wireless Charging Scheme for IoT Networks

Mahamat, M., Jaber, G., & Bouabdallah, A. (2023) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc58020.2023.10182833 · Cited by 2

A Novel Self-Exploration Scheme for Learning Optimal Policies against Dynamic Jamming Attacks in Cognitive Radio Networks

Sudha, Y., & Sarasvathi, V. (2023) · Cybernetics and Information Technologies · doi:10.2478/cait-2023-0040 · Cited by 1

Policy Selection and Scheduling of Cyber-Physical Systems with Denial-of-Service Attacks via Reinforcement Learning

Jin, Z., Li, Q., Zhang, H., Liu, Z., & Wang, Z. (2024) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2024.p0962 · Cited by 1

Distributed denial of service (DDOS) in Internet of Things (IoT) environment: Classification and overview

Ramesh, B., & Thangaraj, S. J. J. (2024) · AIP conference proceedings · doi:10.1063/5.0219386 · Cited by 1

Security Considerations for Internet of Things (IoT): Exploring Quantum Computing Solutions

Achouri, Y., Djellab, R., Hamouid, K., & Berkani, A. (2024) · International Conference on Innovative and Intelligent Information Technologies (IC3IT) · doi:10.1109/ic3it63743.2024.10869424 · Cited by 1

Assessing the Suitability of IoT Security Datasets for IDS

Koppula, M., & Joseph, L. M. I. L. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2200-1.ch012 · Cited by 1

Behavioral Biometrics for IoT Security: A Machine Learning Framework for Smart Homes

Garg, A. (2022) · Journal of recent trends in computer science and engineering. · doi:10.70589/jrtcse.2022.2.7 · Cited by 10

BMGANet: A deep learning model for source code vulnerability detection by integrating token-level and function-level features

Zhu, E., Qu, X., Liu, X., & Li, X. (2026) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2026.110999 · Cited by 1

Ensuring reliable LNG carrier PSC inspections: A knowledge-grounded and reinforcement learning-augmented vision-language framework for decision support

Yang, Q., Gan, L., Liu, C., Gao, S., Zhang, X., & Cai, Y. (2026) · Ocean Engineering · doi:10.1016/j.oceaneng.2026.125177 · Cited by 1

A privacy preserving mechanisms to secure data driven approaches in industrial internet of things: A bibliometric analysis

Kumar, K., & Khari, M. (2026) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-026-02220-y · Cited by 4

Multi-agent reinforcement learning for DoS mitigation in industrial control systems

McGugan, R., Cook, M. M., & Pezaros, D. P. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112668 · Cited by 1

Mobile cellular network security vulnerability detection using machine learning

Zhang, C., Wang, H., & Chen, G. (2023) · International Journal of Information and Communication Technology · doi:10.1504/ijict.2023.10054797 · Cited by 4

Actively Tracking the Best Arm in Non-Stationary Environments with Mandatory Probing

Ghatak, G. (2023) · International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) · doi:10.23919/wiopt58741.2023.10349898 · Cited by 1

A Review of Enhancement in Security of an IoT System Using BlockChain

Karankar, N., & Seth, A. (2024) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl61749.2024.00116 · Cited by 1

Performance Evaluation of Ensemble Machine Learning Techniques for IoT Edge Device Security

Alsharif, M., & Rawat, D. B. (2024) · International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki65561.2024.00045 · Cited by 1

Resilient Distributed Quadrotor AAVs Game: Addressing FDI and Physical Attacks

Geng, M.-J., Ding, H., Yao, X., & Lv, M. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3556228 · Cited by 1

Towards Secure Healthcare IoT-Opportunities and Challenges of Blockchain

Sharma, N., & Mishra, G. K. (2025) · Transactions on computer systems and networks · doi:10.1007/978-981-96-4074-4_4 · Cited by 1

Deep Reinforcement Learning for Secure Internet of Things

Abdel‐Basset, M., Moustafa, N., Hawash, H., & Ding, W. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-89025-4_8 · Cited by 1

Homomorphic Encryption Reference Model for Mobile Cyber Physical Systems

Karim, H., & Rawat, D. B. (2021) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tpsisa52974.2021.00014 · Cited by 1

A top-down survey on securing IoT with machine learning: goals, recent advances and challenges

Iqbal, S., & Qureshi, S. (2022) · International Journal of Wireless and Mobile Computing · doi:10.1504/ijwmc.2022.10046719 · Cited by 3

Achieving Self-Configurable Runtime State Verification in Critical Cyber-Physical Systems

Rivera, A. O. G., & Tosh, D. K. (2022) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm52983.2022.9961037 · Cited by 1

A Futuristic Survey on Learning Techniques for Internet of Things (IoT) Security : Developments, Applications, and ChallengesPreprint

Patel, C., Vyas, S., Saikia, P., kalariya, D., & parmar, N. (2022) · doi:10.36227/techrxiv.19642977 · Cited by 2

Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things

Demir, A. K., & Alam, S. (2021) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-7998-6975-7.ch007

Recent Advances in Socially Relevant Computing: Status and Perspectives

Murray, A., & Rawat, D. B. (2021) · IEEE Global Humanitarian Technology Conference (GHTC) · doi:10.1109/ghtc53159.2021.9612514

Jamming Impact on Wireless Resource Allocation in Wireless Virtualization

Alshaikhi, A., & Rawat, D. B. (2021) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon53757.2021.9666521

Energy Efficient Dynamic Task Offloading for Blockchain-enabled Virtual Wireless Networks

Alshaikhi, A., & Rawat, D. B. (2021) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon53757.2021.9666519

Advancements in Machine Learning Techniques for Optimizing Cognitive Radio Networks: A Comprehensive Review

Niranjani, V., Duraisamy, P., Priyadharshan, M., & Gayathri, B. (2023) · doi:10.53759/acims/978-9914-9946-9-8_20

AI Makes Crypto Evolve

Zolfaghari, B., Nemati, H., Yanai, N., & Bibak, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-44807-2_3

Deep Reinforcement Learning for Distribution Power System Cyber-Resilience via Distributed Energy Resource Control

Corrado, N. E., Livesay, M., Bailey, T., Johnson, J. D., & Levin, D. (2023) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm57358.2023.10333881

Review on IoT Based Smart Traffic Management System.

Cholera, B. J., & Bajeja, S. L. (2024) · International Journal of Advanced Networking and Applications · doi:10.35444/ijana.2024.15418

Big Data y Fog Computing aplicados al sistema de la salud

González, S. G., & Figueredo, D. D. Á. (2023) · Journal TechInnovation · doi:10.47230/journal.techinnovation.v2.n2.2023.106-116

Enhancing Security of IoT Systems Using Machine Learning-Based Blockchain Technology

Ahuja, K., & Joshi, S. (2024) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-6016-3.ch022

Evaluating Cloud Assisted Machine Learning for Internet-of-Things Edge Device Security

Alsharif, M., & Rawat, D. B. (2024) · International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki65561.2024.00046

Benchmarking Machine Learning based Intrusion Detection for IoT Edge Devices

Alsharif, M., & Rawat, D. B. (2024) · International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI) · doi:10.1109/iiki65561.2024.00044

Off-Policy Inspired Imitation Learning for Generation of Adversarial Malware

Mohammed, A. S., & Okafor, E. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96231-8_19

PP-SSFL: A PUF-Paillier Dual-Factor Encryption Secret Sharing Federated Learning Frameworks for IoT

Fang, T., Li, B., Chen, Z., Han, B., Feng, P., & Hu, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9958-2_3

IoT Security Management Using Reinforcement Learning: The Case of Cameroon National Regulatory Compliances.

Kum, K. B., Amaechi, A. O., Emmanuel, P. T., & Victor, P. M. W. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1406000108

BOARD #108: DARE-AI: Discovery, Analysis, Research and Exploration Based Experiential Learning Platform for Teaching Integrated AI and Cybersecurity

Khakurel, U. B., & Rawat, D. B. (2025) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--55925

Exploring Reinforcement Learning for Adaptive Key Refresh in IoT Networks

Heddadji, N. E. I., Bougrine, N., Messai, M., Seba, H., & Amrouche, K. (2025) · ACM SIGCOMM Conference Posters and Demos · doi:10.1145/3744969.3748427

Embedded computer software security monitoring system based on dynamic intelligent algorithm

Hu, W. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11472-3

Energy Optimization in IoT Adaptive Security: a Performance Comparison of Deep Reinforcement Learning Approaches

Arab, A., Jaber, G., & Bouabdallah, A. (2025) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom66362.2025.11197354

CLAM: A CNN-LSTM Attention-based Model for Network Anomaly Detection System

Ren, K., Liu, L., Bai, H., Wen, Y., Huang, X., & Zeng, J. (2025) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus66297.2025.11294319

Intrusion Detection System Based on Pre-trained Language Models and Deep Reinforcement Learning

Ren, K., Liu, L., Bai, H., Wen, Y., Lu, D. X., & Zhang, S. (2025) · IEEE International Conference on Unmanned Systems (ICUS) · doi:10.1109/icus66297.2025.11294779

Hybrid Convolutional–Bidirectional LSTM Model for Intrusion Detection in Unmanned Aerial Vehicle Cyber-Physical Networks

Tharunkumar, E., Deepika, G., H, F. M., & Chellam, M. B. (2025) · International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) · doi:10.1109/icecie66637.2025.11363799

Prioritized defense resource allocation in IoT networks using combinatorial bandits

Xu, S., Su, C., Dong, K., Feng, X., Liu, P., & Xia, H. (2026) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2026.104449

Machine Learning Based Security Enhancement in IoT

Rastogi, D., Sharma, A., Pandey, M. S., Agarwal, J., & Sharma, N. (2025) · Modern Electronics Devices and Intelligent Communication Systems (MEDCOM) · doi:10.1109/medcom67532.2025.11404878

Reinforcement learning: a snapshot bibliometric analysis of the current state-of-the-art

Dimitriou, L., Orfanidou, C. M., Alogdianakis, F., & Pereira, F. C. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26789-5.00013-4

CrossTrust-IoT: An Adaptive Reinforcement Learning Framework for Cross-Chain Trust in Resource-Constrained IoT Networks

Tran, T.-D., Bui, P.-H., Cam, N. T., & Pham, V.-H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3671650

Cascade Machine Learning Framework for Obstructive Coronary Artery Disease Prediction from Quantitative MPI under Class ImbalancePreprint

Sakkara, H., Pruthipanyasakul, N., Kanungsukkasem, N., & Leelanupab, T. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6340586

Design and Implementation of a Deep Learning Embedded Cryptosystem for IoT Applications

Ismail, M., Tadros, D., Madani, M., & Bourennane, E. (2025) · International Conference on Computer, Software and Modeling (ICCSM) · doi:10.1109/iccsm66818.2025.00012

Secure and robust models for energy-efficient communications for the Internet of Things

Mahamat, M. (2023) · theses.fr (ABES) · doi:10.70675/8680c54dza6a2z4843z8c3ez2ec92e59b0c5

Reinforcement Learning for Autonomous Threat Mitigation in AI-Driven Metaverse Environments

Alauthman, M., Hamarsheh, A., Aldweesh, A. Y., Al–Qerem, A., Fararjeh, T. A., & Almomani, O. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7882-4.ch004

Seguridad digital en el IoT integrado con programas computarizados: desafíos, vulnerabilidades y estrategias de mitigación

Yau, R. (2026) · ICuali · doi:10.66707/f29efc95

A Topic Modeling Approach to Identifying Machine Learning Trends in Smart Industry

Nguyen, H.-S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-07721-9_43

Optimized Hybrid Cryptography with Machine Learning for Securing IoT-Driven Banking in Cloud Environments

Yuvarani, R., Mahaveerakannan, R., Balamanigandan, R., & S.Logasakthivel (2025) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss64956.2025.11500829

Internet of Medical Things: Enhancing Security Through Machine Learning

Lazzeretti, R. (2026) · Lecture notes in bioengineering · doi:10.1007/978-3-032-11050-3_12

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