Machine Learning in Cybersecurity Mario Fritz


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Machine Learning in Cybersecurity


Course Description

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:

  1. ML overview
  2. ML for improving security
  3. Attacks on ML models
  4. ML and Privacy


Class Time and Location

Winter Semester 2019/20
Lectures: Thursday, 14:00 - 16:00  (starts at 14:15)
Exercises: Thursday, 16:00 - 18:00  (starts at 16:15)
Location: E91, 0.05

Instructor: Mario Fritz
Teaching Assistants: Tribhuvanesh Orekondy, Hossein Hajipour, Shadi Rahimian, Dingfan Chen

Contact us at: email


Schedule and Syllabus

Event Type Date Description Course Materials
Lecture October 17 Logistics and Course Overview Slides
Tutorial October 17 Primer on Python and Jupyter Slides | Notebook
Lecture October 24    
Tutorial October 24    
Lecture October 31    
Tutorial October 31    


Course Discussions

We encourage you to subscribe to the course mailing list via this interface.

Using this mailing list, you can reach out to other students for course-related discussions or finding team-mates.



  • Programming: Basic programming skills and familiarity with Python. All assignments will be in Python.
  • Machine Learning: Prior knowledge in ML is helpful.
  • Linear Algebra, Probability, Statistics and Calculus: Introductory level.


Project Honor Code

We encourage discussing ideas and concepts with other students to help you learn and better understand the course content. However, the work you submit and present must be original and demonstrate your effort in solving the presented problems. We will not tolerate blatantly using existing solutions (such as from the internet), improper collaboration (e.g., sharing code or experimental data between groups) and plagiarism. If the honor code is not met, no points will be awarded.

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