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. Christian Hammerschmidt, Samuel Marchal, Radu State, Nino Pellegrino, Sicco Verwer, Efficient Learning of Communication Profiles from IP Flow Records, In Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016 p.1-4, IEEE.
  2. Christian Hammerschmidt, Samuel Marchal, Radu State, Sicco Verwer, Behavioral Clustering of Non-Stationary IP Flow Record Data, In 12th International Conference on Network and Service Management CNSM 2016 p.253-257, IEEE.
  3. Nino Pellegrino, Qin Lin, Christian Hammerschmidt, Sicco Verwer, Learning behavioral fingerprints from Netflows using Timed Automata, In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) p.308-316, IEEE.
  4. Daniël Vos, Sicco Verwer, (2023), Optimal Decision Tree Policies for Markov Decision Processes, In Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 p.5457-5465, International Joint Conferences on Artificial Intelligence (IJCAI).
  5. A. Nadeem, Vera Rimmer, Joosen Wouter, S.E. Verwer, Intelligent Malware Defenses, In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) p.217-253, Springer.
  6. C.S. Cao, Simon Schneider, Nicolás E. Díaz Ferreyra, S.E. Verwer, A. Panichella, Riccardo Scandariato, CATMA: Conformance Analysis Tool For Microservice Applications, In ACM/IEEE 46th International Conference on Software Engineering - Demonstrations p.59-63, ACM/IEEE.
  7. Azqa Nadeem, Sicco Verwer, SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting, In Machine Learning and Knowledge Discovery in Databases. p.157-173, Springer.
  8. D.A. Vos, S.E. Verwer, Efficient Training of Robust Decision Trees Against Adversarial Examples, In BNAIC/BeneLearn 2021 p.702-703.
  9. R. Baumgartner, S.E. Verwer, PDFA Distillation via String Probability Queries
  10. A. Nadeem, S.E. Verwer, Shanchieh Jay Yang, Learning About the Adversary, In Autonomous Intelligent Cyber Defense Agent (AICA) Volume 87 p.105-132, Springer.

Dr. S.E. Verwer