On the Performance of Non-Profiled Differential Deep Learning Attacks against an AES Encryption Algorithm Protected using a Correlated Noise Generation based Hiding Countermeasure
Amir Alipoura, Athanasios Papadimitrioub, Vincent Beroullec, Ehsan Aerabid, David Hélye
Univ. Grenoble Alpes, Grenoble INP, LCIS F-26000 Valence, France
aAmir.Alipour@lcis.grenoble-inp.fr
bAthanasios.Papadimitriou@lcis.grenoble-inp.fr
cVincent.Beroulle@lcis.grenoble-inp.fr
dEhsan.Aerabi@lcis.grenoble-inp.fr
eDavid.Hély@lcis.grenoble-inp.fr
ABSTRACT
Recent works in the field of cryptography focus on Deep Learning based Side Channel Analysis (DLSCA) as one of the most powerful attacks against common encryption algorithms such as AES. As a common case, profiling DLSCA have shown great capabilities in revealing secret cryptographic keys against the majority of AES implementations. In a very recent study, it has been shown that Deep Learning can be applied in a non-profiling way (non-profiling DLSCA), making this method considerably more practical, and able to break powerful countermeasures for encryption algorithms such as AES including masking countermeasures, requiring considerably less power traces than a first order CPA attack. In this work, our main goal is to apply the non-profiling DLSCA against a hiding-based AES countermeasure which utilizes correlated noise generation so as to hide the secret encryption key. We show that this AES, with correlated noise generation as a lightweight countermeasure, can provide equivalent protection under CPA and under nonprofiling DLSCA attacks, in terms of the required power traces to obtain the secret key.
Keywords: Deep Learning, Side Channel Analysis, Profiling SCA, non-Profiling SCA, AES encryption algorithm, Hiding-based AES countermeasure
