{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:41:11Z","timestamp":1781534471428,"version":"3.54.5"},"reference-count":65,"publisher":"International Association for Cryptologic Research","issue":"4","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["NWA.1215.18.014"],"award-info":[{"award-number":["NWA.1215.18.014"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IACR CiC"],"accepted":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>Deep neural networks have proven effective for second-order profiling side-channel attacks, even in a black-box setting with 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 on neural network layers combining (or not) unknown and random secret shares, which is a necessary step to defeat, e.g., Boolean masking countermeasures.<\/jats:p>\n                  <jats:p>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 (denoted ExDL-SCA) 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 what relevant features deep neural networks use, and where in the networks relevant features are learned and irrelevant features are compressed. Using our method, evaluators can determine what secret masks are being exploited by the network, which allows for more detailed feedback on the implementations. This paper opens new perspectives for understanding the role of different neural network layers in profiling side-channel attacks<\/jats:p>","DOI":"10.62056\/a0wahee-3","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T23:39:47Z","timestamp":1767915587000},"update-policy":"https:\/\/doi.org\/10.62056\/adfjwm02dj","source":"Crossref","is-referenced-by-count":0,"title":["I Know What Your Layers Did: Layer-wise Explainability of Deep Learning Side-channel Analysis"],"prefix":"10.62056","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3799-7636","authenticated-orcid":false,"given":"Guilherme","family":"Perin","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/027bh9e22","id-type":"ROR","asserted-by":"publisher"}],"name":"Leiden University","place":["Einsteinweg 55, Leiden, 2333 CC, the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1598-8400","authenticated-orcid":false,"given":"Sengim","family":"Karayal\u00e7in","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/027bh9e22","id-type":"ROR","asserted-by":"publisher"}],"name":"Leiden University","place":["Einsteinweg 55, Leiden, 2333 CC, the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7139-732X","authenticated-orcid":false,"given":"Lichao","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/05n911h24","id-type":"ROR","asserted-by":"publisher"}],"name":"Technical University Darmstadt","place":["Pankratiusstra\u00dfe 2, Darmstadt, 64289, Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7509-4337","authenticated-orcid":false,"given":"Stjepan","family":"Picek","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04e90ca47","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Zagreb Faculty of Electrical Engineering and Computing","place":["Unska 3, Zagreb, 10000, Croatia"]},{"id":[{"id":"https:\/\/ror.org\/016xsfp80","id-type":"ROR","asserted-by":"publisher"}],"name":"Radboud University","place":["Toernooiveld 200-222, Nijmegen, 6525 EC, the Netherlands"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"48349","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"ref1:dpa_book","first-page":"338","volume-title":"Power Analysis Attacks: Revealing the Secrets of Smart\n  Cards","author":"Stefan Mangard","year":"2006"},{"key":"ref2:DBLP:conf\/crypto\/KocherJJ99","series-title":"Lecture Notes in Computer Science","isbn-type":"print","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1007\/3-540-48405-1_25","article-title":"Differential Power Analysis","volume":"1666","author":"Paul C. 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