Modules

Prerequisites

Contents

This course covers both theoretical and practical aspects of applying statistical and machine learning methods as a means of extracting secret data for side-channel attacks. Selected statistical as well as clustering methods will be presented and described. The suitability of the methods for determining side-channel leakage sources in attacked designs is discussed. Furthermore, selected countermeasures against physical attacks are presented and their effectiveness is discussed. Main topics covered are: • Evaluation of algorithmic countermeasures such as elliptic curve point blinding, scalar randomization, and scalar splitting on the resistance of cryptographic accelerators for elliptic curve cryptosystems against horizontal side-channel attacks • Importance of analytics in identifying side-channel leakage sources in the early design phase of cryptographic designs

Learning outcomes

Upon completion of the module, students will be familiar with various statistical, machine learning, and clustering methods for analyzing side-channel leakage sources. They are able to apply these methods and evaluate and comparatively discuss their effectiveness in extracting secret data.They will be able to use, understand, and describe selected methods for analyzing a given set of traces. They understand the applicability of the analysis methods as a means to determine the vulnerability of cryptographic designs in their early design phase, using the example of a hardware accelerator of elliptic curve point multiplication for an elliptic curve cryptosystem.

Assessment

• presentation: 5-10 minutes (10%); • written report: 5-10 pages (50%), • implementation: proven correct functionality (40%)

Remarks

• Study programme Cyber Security M.Sc.: Compulsory elective module in complex "Cyber Security Methods" • Study programme Informatik M.Sc.: Compulsory elective module in complex „Praktische Informatik" (level 400)