  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.
