Cryptology ePrint Archive: Report 2019/168

Profiling Side-channel Analysis in the Restricted Attacker Framework

Stjepan Picek and Annelie Heuser and Sylvain Guilley

Abstract: Profiling side-channel attacks represent the most powerful category of side-channel attacks. There, we assume that the attacker has access to a clone device in order to profile the device. Additionally, we assume the attacker to be unbounded in power in an effort to give the worst-case security analysis. In this paper, we start from a different premise and consider an attacker in a restricted setting where he is able to profile only a limited number of measurements. To that end, we propose a new framework for profiling side-channel analysis that we call the Restricted Attacker framework. With it, we enforce the attackers to really conduct the most powerful attack possible but also we provide a setting that inherently allows a more fair analysis among attacks. Next, we discuss the ramifications of having the attacker with unbounded power when considering neural network-based attacks. There, we are able to prove that the Universal Approximation Theorem can result in neural network-based attacks being able to break implementations with only a single measurement. Those considerations further strengthen the need for the Restricted Attacker framework.

Category / Keywords: implementation / Side-channel analysis, Machine learning, Deep learning, Restricted Attacker framework

Date: received 16 Feb 2019, last revised 16 Feb 2019

Contact author: picek stjepan at gmail com, annelie heuser@irisa fr

Available format(s): PDF | BibTeX Citation

Version: 20190220:182849 (All versions of this report)

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