Machine Learning for Computer Security

Overview

SemesterSommer 2020
Course typeLecture
LecturerJun.-Prof. Dr. Wressnegger
AudienceInformatik Master & Bachelor
Credits3 ECTS
Time09:45–11:15 10:30–11:00
Room131, Building 50.34 Online
LanguageEnglish
Linkhttps://campus.kit.edu/campus/all/event.asp?gguid=0x081124DC953D422E807A1CDF784E4F7B
Registrationhttps://ilias.studium.kit.edu/goto.php?target=crs_1095190

Remote Lecture

Due to the COVID-19 outbreak, this course is going to be held remotely. For this, we are recording the lecture and additionally meet for a short live session once a week.

To receive all the necessary information, please subscribe to the mailing list here.

Description

The lecture is about combining the fields of machine learning and computer security in practice. Many tasks in the computer security landscape are based on manual labor, such as searching for vulnerabilities or analyzing malware. Here, machine learning can be used to establish a higher degree of automation, providing more "intelligent" security solutions. However, also systems based on machine learning can be attacked and need to be secured.

The module introduces students to theoretic and practical aspects of machine learning in computer security. We cover basics on features, feature engineering, and feature spaces in the security domain, discuss the application of clustering and anomaly detection for malware analysis and intrusion detection, as well as, the discovery of vulnerabilities using machine learning. Additionally, we discuss the interpretability and robustness of learning-based systems.

Schedule

DateTopicSlidesRecording
29. AprilIntroduction
06. MayMachine Learning 101, ,
13. MayFrom Data to Features, ,
20. MayEfficient String Processing, ,
27. MayAnomaly Detection for Intrusion Detection, ,
03. JuneMalware Classification
10. JuneNo lecture
17. JuneConcept Drift
24. JuneLearning Vulnerable Code Patterns
01. JulyLearning-based Fuzzing
08. JulyAdversarial Machine Learning
15. JulyExplainable Machine Learning
22. JulySummary and Outlook
05. August 19. AugustWritten Exam

Mailing List

News about the lecture, potential updates of the schedule, and additional material are distributed using a separate mailing list. Moreover, the list enables students to discuss topics of the lecture.

You can subscribe here.