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. Yingqian Zhang, Sicco Verwer, Qing Chuan Ye, Auction optimization using regression trees and linear models as integer programs, In Artificial Intelligence Volume 244 p.368-395.
  2. M.P. Roeling, Statistical Analysis in Cyberspace: Data veracity, completeness, and clustering
  3. R. Baumgartner, S.E. Verwer, Learning state machines via efficient hashing of future traces
  4. Thijs Veugen, Jeroen Doumen, Zekeriya Erkin, Nino Pellegrino, Sicco Verwer, Jos Weber, Improved privacy of dynamic group services, In Eurasip Journal on Information Security Volume 2017 p.1-9.
  5. Qin Lin, Sridha Adepu, Sicco Verwer, Aditya Mathur, (2018), TABOR: A Graphical Model-based Approach for Anomaly Detection in Industrial Control Systems, In ASIACCS 2018 - Proceedings of the 2018 ACM Asia Conference on Computer and Communications Security p.525-536, Association for Computing Machinery (ACM).
  6. Jun-Qiang Wang, Fei-Yi Yan, Peng-Hao Cui, Tian Xia, Fu-Dong Cui, Sicco Verwer, Modeling and analysis of non-homogenous fabrication/assembly systems with multiple failure modes, In International Journal of Advanced Manufacturing Technology Volume 94 (2018) p.309–3325.
  7. Clinton Cao, Agathe Blaise, Sicco Verwer, Filippo Rebecchi, (2022), Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster, In Proceedings of the 17th International Conference on Availability, Reliability and Security, ARES 2022, Association for Computing Machinery (ACM).
  8. Laurens Bliek, Arthur Guijt, Sicco Verwer, Mathijs De Weerdt, (2021), Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints, In GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion p.1851-1859, Association for Computing Machinery (ACM).
  9. Rick Wieman, Mauricio Finavaro Aniche, Willem Lobbezoo, Sicco Verwer, Arie van Deursen, An Experience Report on Applying Passive Learning in a Large-Scale Payment Company, In Proceedings - 2017 IEEE International Conference on Software Maintenance and Evolution, ICSME 2017 p.564-573, IEEE.
  10. Vera Rimmer, Azqa Nadeem, Sicco Verwer, Davy Preuveneers, Wouter Joosen, (2022), Open-World Network Intrusion Detection, In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) p.254-283, Springer.

Dr. S.E. Verwer