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.
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.
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.
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.
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.
The Stratosphere Laboratory focus on applied research at the intersection of machine learning, cybersecurity and helping others. As part of our research and social commitment we develop free software tools that can help the community. In this blog we will recount our experience participating in the 2023 (DIMVA) Tool Arsenal with two of our tools: Slips and the AI VPN.
Week 6 of GSoC is finished, and we are approaching the GSoC mid-term evaluations. In this blog, we share the latest updates from our contributors and mentors.
Week 5 of GSoC is finished, the communication was hard this week since some of the mentors were travelling, but being able to work without weekly guidance is a skill to learn.
Week 4 of GSoC is finished, and the work continues. The contributors keep working, and we keep mentoring using a weekly sync-up meeting, Monday for task management and Discord for daily chats.
Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.
Week 3 of GSoC finished, and some nice work has been done so far. The contributors keep working after classes, and we are conducting mentorship. The hard part of mentorship is to actually teach to do things in a certain way and not to provide all the answers.
With the second week of GSoC gone, in this blog, we bring updates on what has been happening, with news from our mentors and contributors.
In this blog, we bring updates on what has been happening during the Google Summer of Code at Stratosphere, with news from our admins, mentors and contributors.
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.
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.
We are happy to announce that Slips will be presented at BlackHat USA 2022 Arsenal in Las Vegas on Thursday, August 11, from 1:00 pm to 2:30 pm: https://bit.ly/SlipsBHUSA22.
Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.
Slips is a machine learning-based intrusion prevention system for Linux and MacOS, developed at the Stratosphere Laboratories from the Czech Technical University in Prague. Slips reads network traffic flows from several sources, applies multiple detections (including machine learning detections) and detects infected computers and attackers in the network. It is easy to extend the functionality of Slips by writing a new module. This blog shows how to create a new module for Slips from scratch.
Our team is excited to share the latest news and features of Slips, our behavioral-based machine learning intrusion detection system.
