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
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.