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.

翻译：物联网环境中新型复杂攻击场景的兴起，要求采用更先进、更智能的网络防御技术，例如各类入侵检测系统。这些系统负责在无需人工干预的情况下检测并缓解物联网网络中的恶意活动。为解决这一问题，近年来提出了深度强化学习来自动应对入侵/攻击。本文对物联网中基于深度强化学习的入侵检测系统进行了全面综述。此外，本综述将最先进的基于深度强化学习的入侵检测方法分为五类，包括无线传感器网络、深度Q网络、医疗保健、混合技术及其他方法。同时，详细阐述了用于评估各方法性能的关键指标，即准确率、召回率、精确率、假阴性率、假阳性率与F-度量值。本文亦总结了相关研究中使用的数据集。