Due to the COVID19 pandemic, the lecture is held as an online course. All lecture units, exercises, and discussions are conducted remotely via online learning tools. Please subscribe to this mailing list to take part in the course.
The lecture is concerned with the combination of machine learning and computer security. Many tasks in computer security, such as the analysis of malicious software or the discovery of vulnerabilities, largely rest on manual work---a tedious and time-consuming process. Methods from machine learning and data mining can help to accelerate this process and make security systems more 'intelligent'. The lecture explores different approaches for constructing such learning-based security systems.
Every week, we organize a question session as a video conference. Here, students can ask questions and discuss topics of the lecture together. The session takes place every Tuesday from 10:00 to 10:30. A link to the video conference is available here. You can get the access code via the mailing list or the chat of the course.
Every week, we also organize an exercise session as a video conference. In this session, students can discuss the exercise sheets and their solutions. The exercises take place on Wednesday from 10:00 to 11:00. A link to the video conference can be found here. You can get the access code via the mailing list or the chat of the course.
There is a mailing list for the lecture. News and updates regarding the schedule are posted to this list. Furthermore, the list allows students to discuss the topics of the lecture. You can subscribe here.
The exam will take place on August 10th at 10:30. It will be an online exam using the system EvaExam. You will have 90 minutes to complete the exam. Formally, it is defined as a "klausurartige Hausarbeit". Further details are published on the mailing list.
Duda, Hart and Stork. Pattern Classification. Wiley & Sons 2001
Shawe-Taylor & Cristianini. Kernel Methods for Pattern Analysis. Cambridge 2004
Gollmann. Computer Security. Wiley & Sons, 2011
Szor. The Art of Computer Virus Research and Defense. Addison-Wesley, 2005
Rieck. Machine Learning for Application-Layer Intrusion Detection, Lulu 2009