mlsanityData to 7 Oct 2026

Reinforcement Learning for IoT Security: A Comprehensive Survey

arXiv 2102.07247 ↗First posted 14 Feb 2021cs.LG cs.AIcited by 21

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Abstract (arXiv)

The number of connected smart devices has been increasing exponentially for different Internet-of-Things (IoT) applications. Security has been a long run challenge in the IoT systems which has many attack vectors, security flaws and vulnerabilities. Securing billions of B connected devices in IoT is a must task to realize the full potential of IoT applications. Recently, researchers have proposed many security solutions for IoT. Machine learning has been proposed as one of the emerging solutions for IoT security and Reinforcement learning is gaining more popularity for securing IoT systems. Reinforcement learning, unlike other machine learning techniques, can learn the environment by having minimum information about the parameters to be learned. It solves the optimization problem by interacting with the environment adapting the parameters on the fly. In this paper, we present an comprehensive survey of different types of cyber-attacks against different IoT systems and then we present reinforcement learning and deep reinforcement learning based security solutions to combat those different types of attacks in different IoT systems. Furthermore, we present the Reinforcement learning for securing CPS systems (i.e., IoT with feedback and control) such as smart grid and smart transportation system. The recent important attacks and countermeasures using reinforcement learning B in IoT are also summarized in the form of tables. With this paper, readers can have a more thorough understanding of IoT security attacks and countermeasures using Reinforcement Learning, as well as research trends in this area.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 14 of its 120 references that are in the map. First posted on arXiv 14 Feb 2021.

Builds on

14 of 120 references.

References · 14

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  4. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  5. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 342
  6. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
  7. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 282
  8. 2018An Introduction to Deep Reinforcement Learningcited by 201
  9. 2019Deep Reinforcement Learning for Cyber Securitycited by 92
  10. 2018IoT Security Techniques Based on Machine Learningcited by 22
  11. 2018Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approachcited by 14
  12. 2015A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networkscited by 3
2 more
  1. 2016Jamming in the Internet of Things: A Game-Theoretic Perspectivecited by 3
  2. 2018A short review on Applications of Deep learning for Cyber securitycited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 5; 2024: 6; 2025: 4; 2026: 1.

21 papers.

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  1. 2023Neurosymbolic Reinforcement Learning and Planning: A Surveycited by 24
  2. 2021Reinforcement Learning for Feedback-Enabled Cyber Resiliencecited by 18
  3. 2023Learning Near-Optimal Intrusion Responses Against Dynamic Attackerscited by 13
  4. 2024Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processescited by 12
  5. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  6. 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
  7. 2024Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guaranteecited by 6
  8. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
  9. 2023The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learningcited by 3
  10. 2022Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networkscited by 2
  11. 2023RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocolscited by 1
  12. 2023CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigationcited by 1
9 more
  1. 2024A Survey for Deep Reinforcement Learning Based Network Intrusion Detectioncited by 1
  2. 2025Predictive Response Optimization: Using Reinforcement Learning to Fight Online Social Network Abusecited by 1
  3. 2025Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applicationscited by 1
  4. 2022Need of 6G for the Metaverse Realizationcited by 0
  5. 2024Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learningcited by 0
  6. 2024Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learningcited by 0
  7. 2025Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directionscited by 0
  8. 2025Method Decoration (DeMe): A Framework for LLM-Driven Adaptive Method Generation in Dynamic IoT Environmentscited by 0
  9. 2026Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Codingcited by 0