Paper 2022/1360

One for All, All for One: A Unified Evaluation Framework for Univariate DPA Attacks

Abstract

Success Rate (SR) is one of the most popular security metrics measuring the efficiency of side-channel attacks. Theoretical expression reveals the functional dependency on critical parameters such as number of measurements and Signal-to-Noise Ratio (SNR), helping evaluators understand the threat of an attack as well as how one can mitigate it with proper countermeasures. However so far, existing works have exposed fundamental problems such as: (i) the evaluations are restricted to a very limited number of distinguishers and the methods in the literature seem specialized (i.e., hard to be extended). (ii) the evaluations assume an a-priori perfect leakage model which lacks practical relevance and ignores the fact that inaccurate profiling may lead to information loss and distorted SR. In this paper, we tackle above problems by providing an evaluation framework where different univariate DPA distinguishers are intuitively unified as linear maximum likelihood attack seeking for the closest `distance' between vectors in Euclidean space. We argue that this is an intrinsic property of the DPA mechanism and is independent of the leakage model. Then, we abstract the concept of SR and derive the theoretical expression in a geometric way. Finally, the theory allows a further study on leakage model where we formalize criterion explaining the impact of model errors as well as guaranteeing robust performance. We transfer the model effects to a degraded SNR parameter. Experimental results are inline with the theory, confirming that our theoretical expression coincides with the empirical ones.