Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey

TL;DR

This survey reviews the application of deep reinforcement learning techniques for intrusion detection in IoT environments, categorizing methods, discussing performance metrics, and summarizing datasets used.

Contribution

It provides a comprehensive classification and analysis of DRL-based IDS methods in IoT, highlighting recent advancements and evaluation metrics.

Findings

DRL methods are categorized into five groups.

Performance metrics like accuracy and F-measure are detailed.

Datasets used in DRL-based IDS are summarized.

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.