Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey

• URL: http://arxiv.org/abs/2405.20038v1
• Date: Thu, 30 May 2024 13:19:23 GMT
• Title: Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey
• Authors: Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari,
• Abstract summary: 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. The most crucial performance metrics, namely accuracy, recall, precision, false negative rate (FNR), false positive rate (FPR), and F-measure, are detailed.
• Score: 0.23408308015481666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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