Strengthening Data-Driven Cybersecurity
This meeting brings important lessons learned and ongoing efforts to move forward in the space of cybersecurity.
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Leertraject
Cybersecurity research and practice have the potential to benefit significantly from advances in AI machine learning, assuming that next generation solutions can learn from and build upon relevant data.
Both benign and malicious behavior is modeled by collecting, synthesizing, analyzing data, etc. Research communities and industry practitioners need and benefit from the trustworthiness and quality of data. In addition, the way we process and leverage cybersecurity relevant data is of crucial importance.
Programme
14.40: Welcome & Registration
15.00: Keynote: “Scans, Sticks and Carrots: Collecting Data to Improve Cybersecurity” (Prof. Michel van Eeten, TU Delft)
15.40: “From Data to Detection: Challenges in Data Collection for ML-based Network Intrusion Detection Systems” (Gints Engelen, KU Leuven)
16.00: “Machine learning in cybersecurity: a case study in virus and malware detection” (Davy Preuveneers, KU Leuven)
16.20: Break & Refreshments
17.00: Public PhD Defense by Victor Le Pochat: Sound Data Sets and Methods for Web Security Research
(17.45: Q&A and discussion, and deliberation by the jury)
18.30: Networking reception
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