# Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey

arXiv: https://arxiv.org/abs/2405.20038 · HTML: https://mlsanity.com/paper/2405.20038/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2024.

## Built on by (most cited first, 7 of 7)

- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models](https://mlsanity.com/paper/2501.13962/) (2025)
- [Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection](https://mlsanity.com/paper/2504.13186/) (2025)
- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)
- [SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments](https://mlsanity.com/paper/2604.06254/) (2026)
- [SubstratumGraphEnv: Reinforcement Learning Environment (RLE) for Modeling System Attack Paths](https://mlsanity.com/paper/2603.01340/) (2026)
- [Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments](https://mlsanity.com/paper/2604.06481/) (2026)

## Abstract (arXiv)

The rise of new complex attacks scenarios in Internet of things (IoT) environments necessitate more advanced and intelligent cyber defense techniques such as various Intrusion Detection Systems (IDSs) which are responsible for detecting and mitigating malicious activities in IoT networks without human intervention. To address this issue, deep reinforcement learning (DRL) has been proposed in recent years, to automatically tackle intrusions/attacks. In this paper, a comprehensive survey of DRL-based IDS on IoT is presented. Furthermore, in this survey, the state-of-the-art DRL-based IDS methods have been classified into five categories including wireless sensor network (WSN), deep Q-network (DQN), healthcare, hybrid, and other techniques. In addition, the most crucial performance metrics, namely accuracy, recall, precision, false negative rate (FNR), false positive rate (FPR), and F-measure, are detailed, in order to evaluate the performance of each proposed method. The paper provides a summary of datasets utilized in the studies as well.