{'fraction_non_alphanumeric': 0.027346177750155375, 'fraction_numerical': 0.0, 'mean_word_length': 5.388888888888889, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
I Know What Your Layers Did: Layer-wise Explainability of Deep Learning Side-channel Analysis

. Masked cryptographic implementations can be vulnerable to higher-order attacks. For instance, deep neural networks have proven eﬀective for second-order proﬁling side-channel attacks even in a black-box setting (no prior knowledge of masks and implementation details). While such attacks have been successful, no explanations were provided for understanding why a variety of deep neural networks can (or cannot) learn high-order leakages and what the limitations are. In other words, we lack the explainability of how neural network layers combine (or not) unknown and random secret shares, which is a necessary step to defeat, e.g., Boolean masking countermeasures. In this paper, we use information-theoretic metrics to explain the internal activities of deep neural network layers. We propose a novel methodology for the explainability of deep learning-based proﬁling side-channel analysis to understand the processing of secret masks. Inspired by the Information Bottleneck theory, our explainability methodology uses perceived information to explain and detect the diﬀerent phenomena that occur in deep neural networks, such as ﬁtting, compression, and generalization. We provide experimental results on masked AES datasets showing where, what, and why deep neural networks learn relevant features from input trace sets while compressing irrelevant ones, including noise. This paper opens new perspectives for the understanding of the role of diﬀerent neural network layers in proﬁling side-channel attacks.