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. R. Baumgartner, S.E. Verwer, Learning state machines via efficient hashing of future traces
  4. 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.
  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. 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.
  7. 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.
  8. 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.
  9. Rickard Karlsson, Laurens Bliek, Sicco Verwer, Mathijs de Weerdt, (2021), Continuous Surrogate-Based Optimization Algorithms Are Well-Suited for Expensive Discrete Problems, In Artificial Intelligence and Machine Learning - 32nd Benelux Conference, BNAIC/Benelearn 2020, Revised Selected Papers p.48-63, Springer.
  10. D.A. Vos, S.E. Verwer, Efficient Training of Robust Decision Trees Against Adversarial Examples, In BNAIC/BeneLearn 2021 p.702-703.

Dr. S.E. Verwer