Cryptology ePrint Archive: Report 2019/054

Deep Learning to Evaluate Secure RSA Implementations

Mathieu Carbone and Vincent Conin and Marie-Angela Cornelie and Francois Dassance and Guillaume Dufresne and Cecile Dumas and Emmanuel Prouff and Alexandre Venelli

Abstract: This paper presents the results of several successful profiled side-channel attacks against a secure implementation of the RSA algorithm. The implementation was running on a ARM Core SC 100 completed with a certified EAL4+ arithmetic co-processor. The analyses have been conducted by three experts' teams, each working on a specific attack path and exploiting information extracted either from the electromagnetic emanation or from the power consumption. A particular attention is paid to the description of all the steps that are usually followed during a security evaluation by a laboratory, including the acquisitions and the observations pre-processing which are practical issues usually put aside in the literature. Remarkably, the profiling portability issue is also taken into account and different device samples are involved for the profiling and testing phases. Among other aspects, this paper shows the high potential of deep learning attacks against secure implementations of RSA and raises the need for dedicated countermeasures.

Category / Keywords: implementation / Side-Channel Attacks, RSA, Deep Learning

Original Publication (in the same form): IACR-CHES-2019

Date: received 18 Jan 2019, last revised 21 Jan 2019

Contact author: e prouff at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20190125:204051 (All versions of this report)

Short URL: ia.cr/2019/054


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