Deep Learning to Evaluate Secure RSA Implementations

Authors

  • Mathieu Carbone SERMA Safety and Security
  • Vincent Conin SERMA Safety and Security
  • Marie-Angela Cornélie CEA LETI
  • François Dassance Thales ITSEF
  • Guillaume Dufresne Thales ITSEF, France
  • Cécile Dumas CEA LETI
  • Emmanuel Prouff ANSSI
  • Alexandre Venelli Thales ITSEF

DOI:

https://doi.org/10.13154/tches.v2019.i2.132-161

Keywords:

Side-Channel Attacks, RSA, Deep Learning

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 preprocessing 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.

Published

2019-02-28

Issue

Section

Articles

How to Cite

Deep Learning to Evaluate Secure RSA Implementations. (2019). IACR Transactions on Cryptographic Hardware and Embedded Systems, 2019(2), 132-161. https://doi.org/10.13154/tches.v2019.i2.132-161