Paper 2020/306

Leakage Assessment in Fault Attacks: A Deep Learning Perspective

Sayandeep Saha, Manaar Alam, Arnab Bag, Debdeep Mukhopadhyay, and Pallab Dasgupta


Generic vulnerability assessment of cipher implementations against fault attacks (FA) is a largely unexplored research area to date. Security assessment against FA is particularly important in the context of FA countermeasures because, on several occasions, countermeasures fail to fulfil their sole purpose of preventing FA due to flawed design or implementation. In this paper, we propose a generic, simulation-based, statistical yes/no experiment for evaluating fault-assisted information leakage based on the principle of non-interference. The proposed exper- iment is oblivious to the structure of countermeasure/cipher under test and detects fault-induced leakage solely by observing the ciphertext dis- tributions. Unlike a recently proposed approach that utilizes t-test and its higher-order variants for detecting leakage at different moments of ciphertext distributions, in this work, we present a Deep Learning (DL) based leakage detection test. Our DL-based detection test is not specific to only moment-based leakages and thus can expose leakages in several cases where t-test based technique demands a prohibitively large number of ciphertexts. We also present a systematic approach to interpret the leakages from DL models. Apart from improving the leak- age detection test, we explore two generalizations of the leakage assess- ment experiment itself – one for evaluating against the Statistical ineffec- tive fault model (SIFA), and another for assessing fault-induced leakages originating from “non-cryptographic” peripheral components of a secu- rity module. Finally, we present techniques for efficiently covering the fault space of a block cipher by exploiting logic-level and cipher-level fault equivalences. The efficacy of DL-based leakage detection, as well as the proposed generalizations, has been evaluated on a rich test-suite of hardened implementations from several countermeasure classes, includ- ing open-source SIFA countermeasures and a hardware security module called Secured-Hardware-Extension (SHE).

Available format(s)
Publication info
Preprint. MINOR revision.
Contact author(s)
sayandeep iitkgp @ gmail com
alam manaar @ gmail com
amiarnabbolchi @ gmail com
dmcseiitkgp @ gmail com
2021-05-25: last of 2 revisions
2020-03-12: received
See all versions
Short URL
Creative Commons Attribution


      author = {Sayandeep Saha and Manaar Alam and Arnab Bag and Debdeep Mukhopadhyay and Pallab Dasgupta},
      title = {Leakage Assessment in Fault Attacks: A Deep Learning Perspective},
      howpublished = {Cryptology ePrint Archive, Paper 2020/306},
      year = {2020},
      note = {\url{}},
      url = {}
Note: In order to protect the privacy of readers, does not use cookies or embedded third party content.