Sicco Verwer

Sicco Verwer is an Associate Professor in machine learning with applications in cyber security and software engineering at TU Delft since 2014. Before this, he has been a postdoctoral researcher for several years at RU Nijmegen, KU Leuven, and TU Eindhoven.
He has worked on several topics in machine learning and is best known for his work in grammatical inference, i.e., learning state machines from trace data. He has researched and implemented several algorithms for learning such models including RTI, which is one of the first that is able to learn timed automata. In 2013, he received a VENI grant from STW to extend this work and apply it in cyber security. Other recent work include several methods for declarative modelling of machine learning problems using mathematical solvers, and making classifiers discrimination-aware.
He teaches two courses in the cyber security master at TU Delft: Cyber Data Analytics and Automated Software Testing and Reverse Engineering.
If you are interested in the research performed by his lab, or joining as PhD or MSc student, please have a look at Sicco's publications and past publicly available MSc and BSc theses.

  1. Qin Lin, Sicco Verwer, Robert Kooij, Aditya Mathur, (2020), Using datasets from industrial control systems for cyber security research and education, In Critical Information Infrastructures Security - 14th International Conference, CRITIS 2019, Revised Selected Papers Volume 11777 p.122-133, Springer.
  2. Yingqian Zhang, Laurens Bliek, Paulo da Costa, Reza Refaei Afshar, Robbert Reijnen, Tom Catshoek, Daniël Vos, Sicco Verwer, Fynn Schmitt-Ulms, More Authors, (2023), The first AI4TSP competition: Learning to solve stochastic routing problems, In Artificial Intelligence Volume 319.
  3. R. Baumgartner, S.E. Verwer, Learning state machines from data streams: A generic strategy and an improved heuristic, In Proceedings of Machine Learning Research Volume 217.
  4. Ligia Maria Moreira Zorello, Laurens Bliek, Sebastian Troia, Tias Guns, Sicco Verwer, Guido Maier, (2022), Baseband-Function Placement with Multi-Task Traffic Prediction for 5G Radio Access Networks, In IEEE Transactions on Network and Service Management Volume 19 p.5104 - 5119.
  5. Laurens Bliek, Arthur Guijt, Rickard Karlsson, Sicco Verwer, Mathijs de Weerdt, (2023), Benchmarking surrogate-based optimisation algorithms on expensive black-box functions, In Applied Soft Computing Volume 147.
  6. Azqa Nadeem, Daniël Vos, Clinton Cao, Luca Pajola, Simon Dieck, Robert Baumgartner, Sicco Verwer, SoK: Explainable Machine Learning for Computer Security Applications, In Proceedings of the 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P) p.221-240, IEEE.
  7. A. Nadeem, S.E. Verwer, Stephen Moskal, Shanchieh Jay Yang, Alert-driven Attack Graph Generation using S-PDFA, In IEEE Transactions on Dependable and Secure Computing Volume 19 p.731-746.
  8. A. Nadeem, S.E. Verwer, Stephen Moskal, Shanchieh Jay Yang, Enabling Visual Analytics via Alert-driven Attack Graphs, In CCS 2021 - Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security p.2420-2422, Association for Computing Machinery (ACM).
  9. A. Nadeem, S.E. Verwer, Shanchieh Jay Yang, SAGE: Intrusion Alert-driven Attack Graph Extractor, In 18th IEEE Symposium on Visualization for Cyber Security p.36-41, IEEE.
  10. A. Nadeem, C.A. Hammerschmidt, C. Hernandez Ganan, S.E. Verwer, Beyond Labeling: Using Clustering to Build Network Behavioral Profiles of Malware Families, In Malware Analysis using Artificial Intelligence and Deep Learning p.381-409, Springer.

Dr. S.E. Verwer