Welcome to the Cyber Analytics lab at Delft University of Technology. We perform research in Artificial Intelligence and Data Science for Cyber Security and Software Engineering. Our main aim is to get knowledge from software data and use that for analysis, anomaly detection, and classification/intrusion detection. That is why our focus is on developing learning algorithms for white-box models that can be understood by developers and security analysts, such as decision trees, temporal heatmaps, and state machines (or deterministic Markov models). Futhermore, we make them learn from streaming data and robust against evasion. Key topics we study are: State Machine Learning, Automated Reverse Engineering, Network Traffic Analytics, Attacker Behaviour Modelling, Declarative/Optimal Machine Learning, Fuzzing/Symbolic Execution, and Adversarial Machine Learning.
Recent News
- Two papers from our lab (by Azqa Nadeem and Daniël Vos) have been accepted at ECML/PKDD 2022!
- Our student Clinton Cao got his paper “Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster” accepted at IWCSEC 2022
- Our student Daniël Vos has won the Best Cybersecurity Master Thesis Award for his thesis entitled “Adversarially Robust Decision Trees Against User-Specified Threat Models” at the CSng workshop 2021.
- Our student Clinton Cao has won the Best Poster Award at the CSng workshop 2021.
- Our student Tom Catshoek together with Sicco Verwer won the LTL track of the Rigurous Examination of Reactive Systems (RERS 2020) challenge!
- We have a new twitter handle @analytics_cyber. Follow us to receive updates about our research endeavours!
- Sicco Verwer and Toon Calders have received the Test of Time Award for their paper Three naive Bayes approaches for discrimination-free classification at ECML-PKDD 2020.