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Project

Safe adoption of machine learning in cybersecurity applications

Over the past few years, the number as well as the sophistication of security threats has been increasing. State-of-the-art security analytics solutions are therefore increasingly depending on machine learning methods to distinguish malicious from normal behavior in diverse contexts. However, with the use of machine learning for security applications on the rise, so are the attacks of cyber- criminals on these methods. Furthermore, there is no set of best practices on how to reduce the attack surface of machine learning methods. This PhD project will investigate the secure adoption of machine learning techniques, and will research, develop and evaluate solutions - including but not limited to middleware - for a variety of cyber-security application cases.

Date:16 Oct 2018 →  16 Oct 2022
Keywords:Adversarial machine learning
Disciplines:Applied mathematics in specific fields, Computer architecture and networks, Distributed computing, Information sciences, Information systems, Programming languages, Scientific computing, Theoretical computer science, Visual computing, Other information and computing sciences
Project type:PhD project