Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully
immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to …

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 …

Deep learning (DL) has gained popularity in network intrusion detection, due to its strong
capability of recognizing subtle differences between normal and malicious network activities …

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and
information or cyber worlds. Their deployment in critical infrastructure have demonstrated a …

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

Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual …

As Deep Learning (DL) is continuously adopted in many industrial applications, its quality
and reliability start to raise concerns. Similar to the traditional software development …

Adversarial attacks of deep neural networks have been intensively studied on image, audio,
and natural language classification tasks. Nevertheless, as a typical while important real …

In a two-player deep reinforcement learning task, recent work shows an attacker could learn
an adversarial policy that triggers a target agent to perform poorly and even react in an …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …