Paper 2019/143

Deep Neural Network Attribution Methods for Leakage Analysis and Symmetric Key Recovery

Benjamin Hettwer, Stefan Gehrer, and Tim Güneysu

Abstract

Deep Neural Networks (DNNs) have recently received significant attention in the side-channel community due to their state-of-the-art performance in security testing of embedded systems. However, research on the subject mostly focused on techniques to improve the attack efficiency in terms of the number of traces required to extract secret parameters. What has not been investigated in detail is a constructive approach of DNNs as a tool to evaluate and improve the effectiveness of countermeasures against side-channel attacks. In this work, we try to close this gap by applying attribution methods that aim for interpreting DNN decisions, in order to identify leaking operations in cryptographic implementations. In particular, we investigate three different approaches that have been proposed for feature visualization in image classification tasks and compare them regarding their suitability to reveal Points of Interests (POIs) in side-channel traces. We show by experiments with three separate data sets that Layer-wise Relevance Propagation (LRP) proposed by Bach et al. provides the best result in most cases. Finally, we demonstrate that attribution can also serve as a powerful side-channel distinguisher in DNN-based attack setups.