# Reinforcement Learning for IoT Security: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2102.07247 · HTML: https://mlsanity.com/paper/2102.07247/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 120 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation](https://mlsanity.com/paper/1604.06057/) (2016)
- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [IoT Security Techniques Based on Machine Learning](https://mlsanity.com/paper/1801.06275/) (2018)
- [Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approach](https://mlsanity.com/paper/1809.05258/) (2018)
- [A Survey on Applications of Model-Free Strategy Learning in Cognitive Wireless Networks](https://mlsanity.com/paper/1504.03976/) (2015)
- [Jamming in the Internet of Things: A Game-Theoretic Perspective](https://mlsanity.com/paper/1607.06255/) (2016)
- [A short review on Applications of Deep learning for Cyber security](https://mlsanity.com/paper/1812.06292/) (2018)

## Built on by (most cited first, 23 of 23)

- [Neurosymbolic Reinforcement Learning and Planning: A Survey](https://mlsanity.com/paper/2309.01038/) (2023)
- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Learning Near-Optimal Intrusion Responses Against Dynamic Attackers](https://mlsanity.com/paper/2301.06085/) (2023)
- [Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processes](https://mlsanity.com/paper/2403.12946/) (2024)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [New Challenges in Reinforcement Learning: A Survey of Security and Privacy](https://mlsanity.com/paper/2301.00188/) (2022)
- [Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee](https://mlsanity.com/paper/2405.15245/) (2024)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning](https://mlsanity.com/paper/2308.16562/) (2023)
- [Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks](https://mlsanity.com/paper/2205.10366/) (2022)
- [RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols](https://mlsanity.com/paper/2302.04519/) (2023)
- [CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation](https://mlsanity.com/paper/2308.05978/) (2023)
- [A Survey for Deep Reinforcement Learning Based Network Intrusion Detection](https://mlsanity.com/paper/2410.07612/) (2024)
- [Predictive Response Optimization: Using Reinforcement Learning to Fight Online Social Network Abuse](https://mlsanity.com/paper/2502.17693/) (2025)
- [Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions](https://mlsanity.com/paper/2503.10058/) (2025)
- [Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications](https://mlsanity.com/paper/2505.19837/) (2025)
- [Need of 6G for the Metaverse Realization](https://mlsanity.com/paper/2301.03386/) (2022)
- [Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning](https://mlsanity.com/paper/2405.12228/) (2024)
- [A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response](https://mlsanity.com/paper/2410.04518/) (2024)
- [Trading Devil RL: Backdoor attack via Stock market, Bayesian Optimization and Reinforcement Learning](https://mlsanity.com/paper/2412.17908/) (2024)
- [Method Decoration (DeMe): A Framework for LLM-Driven Adaptive Method Generation in Dynamic IoT Environments](https://mlsanity.com/paper/2512.21817/) (2025)
- [Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding](https://mlsanity.com/paper/2606.05584/) (2026)
- [Unlearning to Protect: A Distilled Reinforcement Learning Framework with Privacy-Preserving Feature Unlearning and XAI for IoT Security](https://mlsanity.com/paper/2607.07635/) (2026)

## 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.