Slips
Stratosphere IPS
Adaptive Response in Slips IDS as Immune T Cells

The T Cell module was created to give Slips a stateful adaptive response layer on top of its existing evidence pipeline. While the original detectors already provide the innate immune component through PAMP and DAMP evidence, the T Cell module adds antigen recognition, co-stimulation, context evaluation, tolerance, activation, effector action, and memory. It does this by extracting structured antigens from live evidence, matching them against the accepted regex repertoire generated by RegexGenerator, and then combining that recognition with the cumulative danger signaled by recent PAMP and DAMP observations. This allows Slips to move from isolated detections to a more explicit immune decision process that can decide when to ignore, when to contain, and when to remember.

Adaptive Response in Slips IDS as Immune T Cells
Slips
Stratosphere IPS
Adapting Detections in Slips with Immune ...

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 names. It continuously uses the shared LLM service to propose one regex at a time, then applies local validation and negative selection against benign corpora to reject unsafe or overly broad patterns. The accepted regexes become a reusable adaptive recognition repertoire for other modules, especially the T Cell responder.

Adapting Detections in Slips with Immune Pseudo-Generated Regexes
Machine Learning, Python, Slips, Stratosphere Linux IPS
Stratosphere IPS
HTTPS Anomaly Detection in Slips: Adaptive ...

The new HTTPS anomaly detection module in Slips builds per-host adaptive baselines in traffic time, then detects deviations at two levels: per-flow (for bytes to known servers) and per-hour (for host behavior like new servers, unique servers, JA3 changes, and flow volume). It uses online statistics and z-scores for transparent scoring, plus controlled adaptation states (training_fit, drift_update, suspicious_update) to keep learning while reducing poisoning risk.
The result is explainable, operational evidence in clear human text: what changed, confidence, and why it is anomalous.

HTTPS Anomaly Detection in Slips: Adaptive Baselines for Real Traffic
Education, publications, Python, Slips, Stratosphere Linux IPS
Stratosphere IPS
Rethinking Cybersecurity Defense: Principles ...

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

Rethinking Cybersecurity Defense: Principles from Biological Immunity
Maria Rigaki
AI Attackers in Your Pocket: How Small Language ...

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.

Machine Learning

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HTTPS Anomaly Detection in Slips: Adaptive Baselines for Real Traffic

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HTTPS Anomaly Detection in Slips: Adaptive Baselines for Real Traffic

The new HTTPS anomaly detection module in Slips builds per-host adaptive baselines in traffic time, then detects deviations at two levels: per-flow (for bytes to known servers) and per-hour (for host behavior like new servers, unique servers, JA3 changes, and flow volume). It uses online statistics and z-scores for transparent scoring, plus controlled adaptation states (training_fit, drift_update, suspicious_update) to keep learning while reducing poisoning risk.
The result is explainable, operational evidence in clear human text: what changed, confidence, and why it is anomalous.

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NetSecGame - A Framework for Training and Evaluating AI Agents in Network Security Environments

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NetSecGame - A Framework for Training and Evaluating AI Agents in Network Security Environments

We are excited to announce the release of NetSecGame (NSG) v0.1.0, a framework for training and evaluating AI agents in network security environments. Developed at the Stratosphere Laboratory at CTU in Prague, NSG provides a highly configurable testbed for both offensive and defensive security tasks.

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The Attacking Active Directory Game - Can you outsmart the Machine Learning model? Help us by playing the evasion game!

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The Attacking Active Directory Game - Can you outsmart the Machine Learning model? Help us by playing the evasion game!

The “Attacking Active Directory Game” is part of a project where our researcher Ondrej Lukas developed a way to create fake Active Directory (AD) users as honey-tokens to detect attacks. His machine learning model was trained in real AD structures and can create a complete new fake user that is strategically placed in the structure of a company.

Machine Learning Leaks and Where to Find Them

Machine learning systems are now ubiquitous and work well in several applications, but it is still relatively unexplored how much information they can leak. This blog post explores the most recent techniques that cause ML models to leak private data, an overview of the most important attacks, and why this type of attacks are possible in the first place.

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Creating "Too much noise" in DEFCON AI village CTF challenge

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During DEFCON 26 the AI village hosted a jeopardy style CTF with challenges related to AI/ML and security. I thought it would be fun to create a challenge for that and I had an idea that revolved around Denoising Autoencoders (DA). The challenge was named “Too much noise” but unfortunately it was not solved by anyone during the CTF. In this blog I would like to present the idea behind it and how one could go about and solve it.

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