Krček, Marina ; Wu, Lichao ; Perin, Guilherme ; Picek, Stjepan (2024)
Shift-Invariance Robustness of Convolutional Neural Networks in Side-Channel Analysis.
In: Mathematics, 2024, 12 (20)
doi: 10.26083/tuprints-00028672
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure commonly investigated in related works is desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We investigate data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show that the proposed techniques work very well and improve the attack significantly, even for an order of magnitude.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Krček, Marina ; Wu, Lichao ; Perin, Guilherme ; Picek, Stjepan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Shift-Invariance Robustness of Convolutional Neural Networks in Side-Channel Analysis |
| Sprache: | Englisch |
| Publikationsjahr: | 12 November 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 18 Oktober 2024 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Mathematics |
| Jahrgang/Volume einer Zeitschrift: | 12 |
| (Heft-)Nummer: | 20 |
| Kollation: | 17 Seiten |
| DOI: | 10.26083/tuprints-00028672 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/28672 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure commonly investigated in related works is desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We investigate data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show that the proposed techniques work very well and improve the attack significantly, even for an order of magnitude. |
| Freie Schlagworte: | side-channel analysis, deep learning, misalignment, countermeasures, shift-invariance |
| ID-Nummer: | Artikel-ID: 3279 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-286722 |
| Zusätzliche Informationen: | This article belongs to the Special Issue Applications of Artificial Intelligence to Cryptography |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 500 Naturwissenschaften und Mathematik > 510 Mathematik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Systemsicherheit |
| Hinterlegungsdatum: | 12 Nov 2024 13:13 |
| Letzte Änderung: | 13 Nov 2024 10:57 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Shift-Invariance Robustness of Convolutional Neural Networks in Side-Channel Analysis. (deposited 12 Nov 2024 13:13) [Gegenwärtig angezeigt]
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