SNR-centric power trace extractors for side-channel attacks

Abstract

Existing power trace extractors consider the case where the number of power traces available to the attacker is sufficient to guarantee successful attacks, and the goal of power trace extraction is to extract a small part of traces with high Signal-to-Noise Ratio (SNR) to reduce the complexity of attacks rather than to increase the success rates. Although strict theoretical proofs are given, the existing power trace extractors are too simple and leakage characteristics of Points-Of-Interest (POIs) have not been thoroughly analyzed. They only maximize the variance of data-dependent power consumption component and ignore the noise component, which results in very limited SNR that hampers the performance of extractors. In this paper, we provide a rigorous theoretical analysis of SNR of power traces, and propose a simple yet efficient SNR-centric extractor, named Shortest Distance First (SDF), to extract power traces with the smallest estimated noise by taking advantage of known plaintexts. In addition, to maximize the variance of the exploitable component while minimizing the noise, we refer to the SNR estimation model and propose another novel extractor named Maximizing Estimated SNR First (MESF). Finally, we further propose an advanced extractor called Mean-optimized MESF (MMESF) that exploits the mean power consumption of each plaintext byte value to more accurately and reasonably estimate the data-dependent power consumption of the corresponding samples. Experiments on both simulated power traces and measurements from an ATmega328p micro-controller demonstrate the superiority of our new extractors.