Our team
Our projects
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We’ve built many widely used network traffic datasets in cybersecurity. From real malware captures to large-scale traffic collections, our datasets help researchers, students, and practitioners around the world. LEARN MORE.
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Dozens of students have written their theses with us, tackling real problems in network security and machine learning. These projects are more than academic work: they become real contributions to the community. LEARN MORE.
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Our projects range from intrusion detection systems to honeypots and AI-driven tools. What ties them together is our mission: using research and technology to help society defend against digital threats. EXPLORE OUR PROJECTS.
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Our free, open-source machine-learning intrusion prevention system. Built in Stratosphere Lab, Slips drives the innovation in free software IDS technologies with advanced and resilient threat intelligence sharing and threat detection. CHECK OUT SLIPS.
Our latest blogs
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.