  The scale of Internet-connected systems has increased considerably, and these
systems are being exposed to cyber attacks more than ever. The complexity and
dynamics of cyber attacks require protecting mechanisms to be responsive,
adaptive, and scalable. Machine learning, or more specifically deep
reinforcement learning (DRL), methods have been proposed widely to address
these issues. By incorporating deep learning into traditional RL, DRL is highly
capable of solving complex, dynamic, and especially high-dimensional cyber
defense problems. This paper presents a survey of DRL approaches developed for
cyber security. We touch on different vital aspects, including DRL-based
security methods for cyber-physical systems, autonomous intrusion detection
techniques, and multiagent DRL-based game theory simulations for defense
strategies against cyber attacks. Extensive discussions and future research
directions on DRL-based cyber security are also given. We expect that this
comprehensive review provides the foundations for and facilitates future
studies on exploring the potential of emerging DRL to cope with increasingly
complex cyber security problems.
