Cryptology ePrint Archive: Report 2020/373

Tandem Deep Learning Side-Channel Attack Against FPGA Implementation of AES

Huanyu Wang and Elena Dubrova

Abstract: The majority of recently demonstrated deep-learning side-channel attacks use a single neural network classifier to recover the key. The potential benefits of combining multiple classifiers have not been explored yet in the side-channel attack's context. In this paper, we show that, by combining several CNN classifiers which use different attack points, it is possible to considerably reduce (more than 40% on average) the number of traces required to recover the key from an FPGA implementation of AES by power analysis. We also show that not all combinations of classifiers improve the attack efficiency.

Category / Keywords: secret-key cryptography / side-channel attack, CNN, tandem model, FPGA, AES

Date: received 31 Mar 2020, last revised 1 Apr 2020

Contact author: huanyu at kth se,dubrova@kth se

Available format(s): PDF | BibTeX Citation

Version: 20200402:122818 (All versions of this report)

Short URL: ia.cr/2020/373


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