Sicco Verwer

Sicco Verwer's picture

Sicco Verwer

T
+31 (0)15 27 88435
E
S.E.Verwer@tudelft.nl
Blog
 
Faculty of Electrical Engineering, Mathematics and Computer Science
Intelligent Systems - Cyber Security
Mekelweg 4
2628 CD Delft
The Netherlands

Office: HB11.130

Research Profile

I am an assistant professor in cyber security. My expertise is in grammatical inference, machine learning, sequential data mining, and artificial intelligence. In my research, I develop methods for learning complex state machines (such as timed automata), strive to use machine learning for more than just prediction (for instance model checking), and investigate the power of search methods (using SAT-solvers and Mixed Integer Programming) in machine learning. I apply my work in cyber security, developing methods for automated reverse engineering of software communication protocols and anomaly detection in network traffic.

News

My article on white-box optimization using automatically learned models has been accepted for Artificial Intelligence journal! See Elsevier ScienceDirect.

Projects

I got awarded a VENI grant from STW for my project on Learning State Machines for Network Traffic Analysis (MANTA)! Together with the national cyber security centre at the department of security and justice in the Netherlands and Surfnet, I will investigate methods for learning timed state machines automatically from network traffic, and searching for malicious behavior in these machines using modern model checkers.

My LEMMA project (Learning Extended State Machines for Malware Analysis) got awarded in last year's NWO cybersecurity call! Together with Frits Vaandrager and Erik Poll from Radboud University Nijmegen, and experts from Madison-Gurkha, Thales, the department of security and justice (WODC and NCSC), and Surfnet, I will build tools for learning state machines from network data and combining the knowledge contained in them using information fusion techniques. The goals are to localize the presence of Malware within a network infrastructure and to pinpoint possible sources for these infections.

Together with Peter Lucas and Arjen Hommerson from Radboud University Nijmegen, I work on the Careful project where we investigate methods for automatically learning a followed medical protocol (essentially a state machine) from patient data from the NIVEL institute. Such models will be used to highlight differences between the envisioned and actual models of care.