Deep Learning-Based Side-Channel Analysis Against AES Inner Rounds

Deep Learning-Based Side-Channel Analysis Against AES Inner Rounds

Swaminathan, Sudharshan (Student TU Delft)
Chmielewski, Łukasz (Riscure; Radboud Universiteit Nijmegen)
Perin, G. (TU Delft Cyber Security)
Picek, S. (TU Delft Cyber Security)

Zhou, Jianying (editor)
Chattopadhyay, Sudipta (editor)
Adepu, Sridhar (editor)
Alcaraz, Cristina (editor)
Batina, Lejla (editor)
Casalicchio, Emiliano (editor)
Jin, Chenglu (editor)
Lin, Jingqiang (editor)
Losiouk, Eleonora (editor)
Majumdar, Suryadipta (editor)
Meng, Weizhi (editor)
Picek, Stjepan (editor)
Zhauniarovich, Yury (editor)
Shao, Jun (editor)
Su, Chunhua (editor)
Wang, Cong (editor)
Zonouz, Saman (editor)

2022

Side-channel attacks (SCA) focus on vulnerabilities caused by insecure implementations and exploit them to deduce useful information about the data being processed or the data itself through leakages obtained from the device. There have been many studies exploiting these leakages, and most of the state-of-the-art attacks have been shown to work on AES implementations. The methodology is usually based on exploiting leakages for the outer rounds, i.e., the first and the last round. In some cases, due to partial countermeasures or the nature of the device itself, it might not be possible to attack the outer rounds. In this case, the attacker needs to resort to attacking the inner rounds. This work provides a generalization for inner round side-channel attacks on AES and experimentally validates it with non-profiled and profiled attacks. We formulate the computation of the hypothesis values of any byte in the intermediate rounds. The more inner the AES round is, the higher is the attack complexity in terms of the number of bits to be guessed for the hypothesis. We discuss the main limitations for obtaining predictions in inner rounds and, in particular, we compare the performance of Correlation Power Analysis (CPA) against deep learning-based profiled side-channel attacks (DL-SCA). We show that because trained deep learning models require fewer traces in the attack phase, they also have fewer complexity limitations to attack inner AES rounds than non-profiled attacks such as CPA. This paper is the first to propose deep learning-based profiled attacks on inner rounds of AES to the best of our knowledge.

http://resolver.tudelft.nl/uuid:7a828894-7ffb-4008-b3bf-bed324a92218

https://doi.org/10.1007/978-3-031-16815-4_10

Springer, Cham

2023-07-01

978-3-031-16814-7

Applied Cryptography and Network Security Workshops - ACNS 2022 Satellite Workshops, AIBlock, AIHWS, AIoTS, CIMSS, Cloud S and P, SCI, SecMT, SiMLA, Proceedings

Satellite Workshops on AIBlock, AIHWS, AIoTS, CIMSS, Cloud S and P, SCI, SecMT, SiMLA 2022, held in conjunction with the 20th International Conference on Applied Cryptography and Network Security, ACNS 2022, 2022-06-20 → 2022-06-23, Virtual, Online

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 0302-9743, 13285

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 Sudharshan Swaminathan, Łukasz Chmielewski, G. Perin, S. Picek