News
October Exam date PreferencesWritten on 14.07.20 by Tribhuvanesh Orekondy Dear Students, We hope you are all well during these extraordinary times. As announced earlier the remaining exams are postponed to the examination 29 & 30 October 2020 Please use the form below to enter… Read more Dear Students, We hope you are all well during these extraordinary times. As announced earlier the remaining exams are postponed to the examination 29 & 30 October 2020 Please use the form below to enter your exam date preference: https://forms.gle/vFJ1tTgJKkuTnuQf8 If you are not comfortable with the google form, you can alternatively email your information and preferred date(s) to: mlcysec_ws1920_staff@lists.cispa.saarland with subject: "MLCySec1920 Oct. Exam Date Preference - FirstName LastName" |
Final Project: Report and PresentationsWritten on 30.01.20 by Tribhuvanesh Orekondy Report
Project Presentations
Report
Project Presentations
Additional details available in today's lecture slides. |
Register for ExamWritten on 28.01.20 by Tribhuvanesh Orekondy By now, you should have received your examination slots by email (the address entered in the google form). Important:
|
Advertisement: CISPA Young Researcher Security Convention SeCon 2020Written on 17.01.20 by Mario Fritz Application is open and seats are still available: https://cispa.saarland/secon/ |
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:
- ML overview
- ML for improving security
- Attacks on ML models
- 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 | ML Overview | Slides |
| Tutorial | October 24 | Scikit-learn, and Pytorch basics | Notebook |
| Lecture | October 31 | DL overview | Slides |
| Tutorial | October 31 | Applying Deep Learning | Notebook |
| Lecture | Nov 7 | Anomaly Detection | Slides |
| Tutorial | Nov 7 | (No tutorial) | |
| Project 1 | (Due 13-Nov 23:59) | Notebook | |
| Lecture | Nov 14 | Evasion Attacks | Slides |
| Tutorial | Nov 14 | GPU cluster, Project 2 Handout | |
| Lecture | Nov 21 | Poisoning | Slides |
| Tutorial | Nov 21 | Project 1: Summary and Discussion | Slides |
| Using the GPU Machines | Slides | ||
| Project 2 | (Due 28-Nov 13:59) | ||
| Lecture | Nov 28 | Game Theory, Membership Inference | Slides |
| Tutorial | Nov 28 | Project 3 Handout | |
| Project 3 | (Due 12-Dec 13:59) | Notebook | |
| Lecture | Dec 5 | (No Lecture) | |
| Tutorial | Dec 5 | Project 2: Summary and Discussion | Slides |
| Lecture | Dec 12 | Model Stealing, Water Marking | Slides |
| Tutorial | Dec 12 | (No Tutorial) | |
| Project 4 | (Multiple deadlines - see handout) | Handout (v1.0) | |
| Lecture | Dec 19 | GAN, Malware (partial) | |
| Lecture | Jan 9 | Malware (cont), Differential Privacy | Slides_Malware, Slides_DP |
| Lecture | Jan 16 | Differential Privacy (continued) | Slides |
| Lecture | Jan 23 | (No Lecture) | |
| Lecture | Jan 30 | Federated Learning | Slides |
| Lecture | Feb 6 | Final Project Presentations (replaces lecture+tutorial) |
Course Discussions
We encourage you to subscribe to the course mailing list via this interface.
mlcysec_ws1920_stud@lists.cispa.saarland
Using this mailing list, you can reach out to other students for course-related discussions or finding team-mates.
Prerequisites
- 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.