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.

research

Poster: Multi-Objective Model Selection Pipeline for Network Flow Classification at POSTERS 2026

Poster: Multi-Objective Model Selection Pipeline for Network Flow Classification at POSTERS 2026

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 (memory & compute efficiency, data ingestion, parallel training, and hyperparameter optimization). Existing ad-hoc scripts make it hard to reproduce results or compare models systematically across these conditions. An extendable machine learning pipeline is developed to address both, targeting malicious network flow classifiers for the Stratosphere Linux IPS (Slips). The output is a set of best-performing models at different FPR and F1 thresholds suitable for deployment in Slips.

Towards Better Understanding of Cybercrime: The Role of Fine-Tuned LLMs in Translation

Towards Better Understanding of Cybercrime: The Role of Fine-Tuned LLMs in Translation

Our paper explores the use of Large Language Models as mechanisms to translate public hacktivists messages from Russian to English as a way to address all these problems. We show how our method can achieve high-fidelity translations and significantly reduce costs by a factor ranging from 430 to 23,000 compared to a human translator.

Paper: A Study of Machete Cyber Espionage Operations in Latin America

On Wednesday, October 2, at the 29th Virus Bulletin International Conference (VB2019) in London, our researchers Veronica Valeros, Maria Rigaki, Kamila Babayeva and Sebastian Garcia will present the results of more than 3 years of studying and tracking the Machete APT in their talk “A Study of Machete Cyber Espionage Operations in Latin America”.

Upcoming Talk: A Study of Machete Cyber Espionage Operations in Latin America

On Wednesday, October 2, at the 29th Virus Bulletin International Conference (VB2019) in London, our researchers Veronica Valeros, Maria Rigaki, Kamila Babayeva and Sebastian Garcia will present the results of more than 3 years of studying and tracking the Machete APT in their talk “A Study of Machete Cyber Espionage Operations in Latin America”.

Protecting the civil society through high quality research