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Machine Learning in Cybersecurity

Recent advances in Machine Learning have led to strong performance in a wide range of tasks which led to a wide spread deployement roll out of such systems. ChatGPT and CoPilots for code and office applications just being a few prominent examples. In terms of privacy and security, this is a double-edged sword. ML techniques can be used to efficiently detect and prevent attacks (e.g., intrusion detection). However, their deployment to many real-world sensitive systems (e.g., self-driving cars, the cloud) also makes them susceptible to numerous attacks. As AI and ML becomes part of our IT infrastructure, we have to know and defend against cybersecurity threats.

The course explores in-depth both of these sides to Machine Learning and Cyber Security. The content addresses the following areas:

  1. ML recap
  2. ML for improving security
  3. Attacks on ML
  4. Defenses for ML
  5. ML and Privacy
  6. Security of Large Language Models

While we do a very brief recap in the beginning, the course requires knowledge on Machine Learning.

Date for lecture: Tuesdays noon to 2pm. 

Date for exercise: to be determined

The course requires prior knowledge on Machine Learning!

Once you have registered - you have access to the internal pages with further information / material (under construction).

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