TUM Logo

ML- and IT Security

ML- and IT Security  

Vorlesung 2+2 SWS / 5 ECTS
Veranstalter: Claudia Eckert
Zeit und Ort:

Ansprechpartner: Nicolas Müller - Fraunhofer AISEC


Geplante Vorlesungszeiten: Montag, 10.00 Uhr bis 12.00 Uhr im Seminarraum 00.13.009A

                                                  Donnerstag, 10.00 Uhr bis 12.00 Uhr im Hörsaal 00.04.011


This lecture provides an overview over the application of machine learning (ML) to IT-Security, and security-aspects of machine-learning.


The lecture is given in english


  • Overview of the application areas 'ML for IT security' and 'IT security for ML'.

  • Fundamentals and advanced concepts for anomaly detection, including both semi- and unsupervised algorithms

  • Basics of dimension reduction for data visualization and preprocessing

  • Fundamentals of Natural Language Processing (NLP) for IT Security: Spam and Malware Detection using Learning Algorithms

  • Basic and advanced concepts of adversarial machine learning: attackability of systems using white/grey/black box attacks and corresponding defenses

  • Possibilities and limitations of transferability of such attacks from the lab to the real world

  • Basic concepts of both creation and detection of audio deepfakes (spoofing) incl. extraction of corresponding features (such as MFCC)


The examination performance is evaluated via a written exam (75min). Knowledge questions test the familiarity with basic concepts and methodological approaches of the following topics: 'ML for IT Security', 'IT Security for ML' and 'Audio Deepfakes / Spoofing'. The ability to design anomaly detection systems will be tested. Furthermore, small exercise scenarios will be used to evaluate the student’s ability to design attacks on different ML algorithms and to design appropriate defense strategies. Fundamentals in audio deepfake detection and creation, including feature extraction will be reviewed using knowledge questions

IN0001 and one of the following is recommended:

·       IN2332, Statistical Modeling and Machine Learning

·       IN2346, Introduction to Deep Learning

After successful completion of this module, participants will have an overview of three areas in the intersection of IT security and machine learning: 


1.     Machine learning for IT security, i.e., the application of ML for enhancing security in information systems. This includes anomaly detection algorithms for detecting e.g. network attacks, but also basic approaches for spam and malware detection. Classical methods for detecting attacks, spam or malware (e.g. signature-based algorithms) are not covered. 


2.     IT Security for ML: Students will know basic and advanced attacks on ML systems under different threat models and will be able to describe and classify their advantages and disadvantages. They can describe defenses and their limitations and evaluate their effectiveness. 


3.     Deepfake / Audio Spoofing: Participants will know the basic concepts in audio deepfake / audio spoofing. This includes both the creation and detection of audio deepfakes using ML. Students will be familiar with the most basic signal processing techniques as they pertain to feature extraction.




  • Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer, 2006.

Ian J. Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning. MIT Press, 2016.