Paper 2019/168

Profiling Side-channel Analysis in the Efficient Attacker Framework

Stjepan Picek, Annelie Heuser, Guilherme Perin, 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 to profile its leaking behavior. Additionally, we consider the attacker to be unbounded in power to give the worst-case security analysis. In this paper, we start with a different premise where we are interested in the minimum strength that the attacker requires to conduct a successful attack. To that end, we propose a new framework for profiling side-channel analysis that we call the Efficient Attacker Framework. With it, we require the attackers to use as powerful attacks as possible, but we also provide a setting that inherently allows a more objective analysis among attacks. We discuss the ramifications of having the attacker with unlimited power when considering the neural network-based attacks. There, we show that the Universal Approximation Theorem can be connected with neural network-based attacks able to break implementations with only a single measurement. Those considerations further strengthen the need for the Efficient Attacker Framework. To confirm our theoretical results, we provide an experimental evaluation of our framework.