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Machine Learning in Cyber Security
Recent advances in Machine Learning has lead to near (or beyond) human-level performance in many tasks - autonomous driving, voice assistance, playing a variety of games. 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, such as introducing imperceptible perturbations in inputs and forcing ML systems behave in unintended ways.
The course explores in-depth both of these sides to Machine Learning and Cyber Security. The content addresses the following areas:
- ML overview
- ML for improving security
- Attacks on ML
- Defenses for ML
- ML and Privacy
While we do a brief recap in the beginning, the course requires knowledge on Machine Learning.
Date for lecture: Tuesdays noon to 2pm.
Date for exercise: Fridays 2pm to 4pm
Due to the size of the course - the lecture will start in an online format until further notice.
The course requires prior knowledge on Machine Learning.
Once you have registered - please find internal information and schedule and links here (under construction).