Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …

Cyberattack simulations appear across multiple computer security domains and are
interpreted in many different but equally viable ways. However, this makes the topic appear …

To practically leverage a dataset, various attack situations should be created according to
the user's objective and how realistic the generated attack sequence is should be …

The MITRE ATT&CK framework, a comprehensive knowledge base of adversary tactics and
techniques, has been widely adopted by the cybersecurity industry as well as by academic …

Large Language Models (LLMs) have gained widespread popularity across diverse
domains involving text generation, summarization, and various natural language processing …

Current frameworks for training offensive penetration testing agents with deep reinforcement
learning struggle to produce agents that perform well in real-world scenarios, due to the …

Cyber-Physical Systems (CPS) are safety critical as they can be at risk for their users and
environment. It is thus paramount to assess all risks related to their use, including the risks of …

The ongoing rise in cyberattacks and the lack of skilled professionals in the cybersecurity
domain to combat these attacks show the need for automated tools capable of detecting an …

One of the contemporary grand challenges in cybersecurity research is designing and
evaluating effective attack strategies on network infrastructures performed by autonomous …

Abstract SOC (Security Operation Center) analysts historically struggled to keep up with the
growing sophistication and daily prevalence of cyber attackers. To aid in the detection of …