Open AccessDeep Learning to Evaluate Secure RSA Implementations
Author(s) -
Mathieu Carbone,
Vincent Conin,
Marie-Angela Cornélie,
François Dassance,
Guillaume Dufresne,
Cécile Dumas,
Emmanuel Prouff,
Alexandre Venelli
Publication year - 2019
Publication title -
iacr transactions on cryptographic hardware and embedded systems
Language(s) - English
Resource type - Journals
ISSN - 2569-2925
DOI - 10.46586/tches.v2019.i2.132-161
Subject(s) - computer science , implementation , software portability , profiling (computer programming) , side channel attack , computer security , preprocessor , power consumption , cryptography , deep learning , embedded system , computer engineering , artificial intelligence , software engineering , power (physics) , operating system , physics , quantum mechanics
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.