Deep Learning Side-Channel Collision Attack

Abstract: With the breakthrough of Deep Neural Networks, many fields benefited from its enormously increasing performance. Although there is an increasing trend to utilize Deep Learning (DL) for Side-Channel Analysis (SCA) attacks, previous works made specific assumptions for the attack to work. Especially the concept of template attacks is widely adapted while not much attention was paid to other attack strategies. In this work, we present a new methodology, that is able to exploit side-channel collisions in a black-box setting. In particular, our attack is performed in a non-profiled setting and requires neither a hypothetical power model (or let’s say a many-to-one function) nor details about the underlying implementation. While the existing non-profiled DL attacks utilize training metrics to distinguish the correct key, our attack is more efficient by training a model that can be applied to recover multiple key portions, e.g., bytes. In order to perform our attack on raw traces instead o.... https://tches.iacr.org/index.php/TCHES/article/view/10969

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Deep Learning Side-Channel Collision Attack ; volume:2023 ; number:3 ; year:2023 ; IACR transactions on cryptographic hardware and embedded systems ; 2023, Heft 3 (2023)

Beteiligte, Orts- und Zeitangaben

• Urheber: Staib, Marvin ; Moradi, Amir

Weitere Informationen

• DOI: 10.46586/tches.v2023.i3.422-444
• URN: urn:nbn:de:101:1-2023102518595541839628
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:58 MESZ