The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …

The cybersecurity threat landscape has lately become overly complex. Threat actors
leverage weaknesses in the network and endpoint security in a very coordinated manner to …

Reinforcement learning (RL) has been demonstrated suitable to develop agents that play
complex games with human-level performance. However, it is not understood how to …

Despite the successful application of machine learning (ML) in a wide range of domains,
adaptability—the very property that makes machine learning desirable—can be exploited by …

The incorporation of advanced networking technologies makes modern systems vulnerable
to cyber-attacks that can result in a number of harmful outcomes. Due to the increase of …

Given the success of reinforcement learning (RL) in various domains, it is promising to
explore the application of its methods to the development of intelligent and autonomous …

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was
inspired by recent developments in the fields of reinforcement learning (RL) and cyber …

The rise of the new generation of cyber threats demands more sophisticated and intelligent
cyber defense solutions equipped with autonomous agents capable of learning to make …

While Deep Reinforcement Learning (DRL) has achieved outstanding performance in
extensive applications, exploiting its vulnerability with adversarial attacks is essential …

Machine learning (ML) and deep learning (DL) both drawn unparalleled community interest
recently. With a growing convergence of online activities and digital life, the way people …