04.E.360, Building 28, EEMCS,
Delft University of Technology,
My research focuses on Explainable Machine Learning for Cyber Security. I truly believe that machine learning can provide more insights than just prediction probabilities. To this aim, I develop explainable sequential machine learning pipelines that extract actionable intelligence from large volumes of cyber data with the aim of assisting security analysts in their daily operations. These pipelines create human in the loop settings for AI-assisted humans.
Next to research, I spend 40% of my time developing and teaching Cyber Security lectures to BSc. (Computer Science and Engineering) students at TU Delft. The teaching material can be found under Cyber Security Lecture Series.
Outside of work, I love landscape photography and traveling. I enjoy discussions about human psychology, devious behavior, cats, imposter syndrome, and… well… cats.
|05-2023||Submitted the first draft of my thesis to my promotor!|
|04-2023||Won the “Best Demo Award” for our alert-driven attack graph generator at ICT.Open 2023!|
|04-2023||Teaching cybersecurity to the Executive MSc. students at Leiden University.|
|03-2023||Hosted WICCA for a meetup for Women in Cybersecurity on “Cybersecurity in the AI age”.|
|02-2023||Our SoK paper on XAI for cybersecurity has been accepted at EuroS&P!|
EuroS&PSoK: Explainable Machine Learning for Computer Security ApplicationsIn IEEE European Symposium on Security and Privacy (Euro S&P), 2023
ECMLSECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids
and Medoid VotingIn Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2022
TDSCAlert-driven Attack Graph Generation using S-PDFAIn IEEE Transcations on Dependable and Secure Computing, 2021
MAAIDLBeyond Labeling: Using Clustering to Build Network Behavioral Profiles of
Malware FamiliesIn Malware Analysis Using Artificial Intelligence and Deep Learning, 2021
CCSEnabling Visual Analytics via Alert-driven Attack GraphsIn Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021