Vera Rimmer
Research Expert at DistriNet, KU Leuven, Belgium

I conduct and lead research activities at the intersection of security, privacy, and AI. Our team explores data analytics in network intrusion and malware detection, privacy-enhancing technologies, and trustworthiness of data-driven AI in the wider ICT context. The driving force behind my work is examining the optimal role of technology, particularly AI, in our society while balancing its benefits and challenges. In this age of surveillance and uncontrolled data collection and inference, I am committed to developing a comprehensive understanding, forming reasonable expectations, and devising effective strategies to mitigate the risks of applied AI.
Beyond academic research, our team is committed to sharing scientific knowledge through direct collaboration with industry. We work with companies seeking practical guidance on how to safely and securely leverage modern AI technologies, such as deep learning, foundation models, and large language models, to meet their needs.
What's new?
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Selected publications
- PETSTrace Oddity: Methodologies for Data-Driven Traffic Analysis on TorProceedings on Privacy Enhancing Technologies, 2022
- WoRMAPosition Paper: On Advancing Adversarial Malware Generation Using Dynamic FeaturesIn Proceedings of the 1st Workshop on Robust Malware Analysis, 2022
- SpringerOpen-World Network Intrusion DetectionIn Security and Artificial Intelligence: A Crossdisciplinary Approach, 2022
- WTMCTroubleshooting an intrusion detection dataset: the CICIDS2017 case studyIn 2021 IEEE Security and Privacy Workshops (SPW), 2021
- NDSSAutomated Website Fingerprinting through Deep LearningIn Proceedings of the 25nd Network and Distributed System Security Symposium (NDSS 2018), 2018
- WOOTFishy faces: Crafting adversarial images to poison face authenticationIn 12th USENIX Workshop on Offensive Technologies (WOOT 18), 2018