Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Small satellites are increasingly vulnerable to cyberattacks, yet their resource constraints make implementing robust security mechanisms a significant challenge. This thesis explores how to protect the...

Training classifiers for network intrusion detection is hindered by two types of problems: data challenges (lack of labels, class imbalance, non-IID data, and concept drift) and engineering challenges...

Sebastian has just been awarded by the Dean of the Faculty of Electrical Engineering, Czech Technical University in Prague, for his outstanding teaching performance in the Winter Semester 2025/2026!

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

NetSecGame v0.2.0 is here. This release focuses on what matters most for reproducible AI research: deterministic episode control, a more robust simulation server, and a significantly expanded test suite....

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Build, train, and evaluate network security agents using NetSecGame. A high-speed, Docker-ready framework for Reinforcement Learning and cyber research.

The T Cell module was created to give Slips a stateful adaptive response layer on top of its existing evidence pipeline.

The RegexGenerator module was created to give Slips an adaptive way to discover new string-based detectors for changing indicators such as domains, URIs, filenames, TLS SNI values, and certificate common...

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

The new HTTPS anomaly detection module in Slips builds per-host adaptive baselines in traffic time, then detects deviations at two levels.

Our research identifies sixteen fundamental principles of biological immunity and translates them into cybersecurity defense architectures that emphasize multi-dimensional coordination over single-…

Build, train, and evaluate network security agents using NetSecGame. A high-speed, Docker-ready framework for Reinforcement Learning and cyber research.

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

Every year, hundreds of students start Introduction to Security — a 14-week, hands-on course where curiosity meets courage, and learning means breaking, building, and defending…

We are pleased to announce the publication of our latest paper, “ Building adaptive and transparent cyber agents with local language models ,” in the Journal of Expert Systems with Applications.

Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.

YouTube video by The Honeynet Project

YouTube video by The Honeynet Project