OBJECTIVE

Side-channel attacks (SCAs) are powerful attacks based on the information obtained from the implementation of cryptographic devices. Profiling side-channel attacks have received a lot of attention in the last years because this type of attacks defines worst-case security assumptions. The SCA community realized that the same profiling approach is used in other domains in the form of supervised machine learning.
Consequently, some researchers started experimenting with different machine learning techniques and evaluating their effectiveness in the SCA context. More recently, we are witnessing the uprise of deep learning techniques in the SCA community with strong results in side-channel analyses, even in the presence of countermeasures.

In this training, we will concentrate on advanced topics in profiling SCAs where the emphasis will be on machine learning and deep learning techniques. During the training, the students will have the opportunity to learn theoretical background on several machine learning techniques but also have to implement them and use in SCAs. There, we will cover not only the attack phase but also pre-processing and feature engineering phases. Besides more "traditional" machine learning techniques, we will cover various deep learning techniques and investigate how to use them in practical SCAs.
While the emphasis of the training is on side-channel analysis, we will also discuss fault injection attacks and how artificial intelligence techniques can be used there. During the training, the students will have the opportunity to develop several side-channel tools in Python and perform attacks to obtain the secret key.

Course Sections:

Who should attend the course:

• Researchers and students who want to learn the advanced techniques of side-channel analysis
• Penetration testers, auditors and evaluators of secure embedded devices
• Developers of secure IoT products
• Any embedded security enthusiast or machine learning enthusiast