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Learn from Your Faults: Leakage Assessment in Fault Attacks Using Deep Learning

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Abstract

Generic vulnerability assessment of cipher implementations against Fault Attacks (FA) is a largely unexplored research area. Security assessment against FA is critical for FA countermeasures. On several occasions, countermeasures fail to fulfil their sole purpose of preventing FA due to flawed design or implementation. This paper proposes a generic, simulation-based, statistical yes/no experiment for evaluating fault-assisted information leakage based on the principle of non-interference. It builds on an initial idea called ALAFA that utilizes t-test and its higher-order variants for detecting leakage at different moments of ciphertext distributions. In this paper, we improve this idea with a Deep Learning (DL)-based leakage detection test. The DL-based detection test is not specific to only moment-based leakages. It thus can expose leakages in several cases where t-test-based technique demands a prohibitively large number of ciphertexts. Further, we present two generalizations of the leakage assessment experiment—one for evaluating against the statistical ineffective fault model and another for assessing fault-induced leakages originating from “non-cryptographic” peripheral components of a security module. Finally, we explore techniques for efficiently covering the fault space of a block cipher by exploiting logic-level and cipher-level fault equivalences. The efficacy of our proposals has been evaluated on a rich test suite of hardened implementations, including an open-source Statistical Ineffective Fault Attack countermeasure and a hardware security module called Secured-Hardware-Extension.

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Notes

  1. The leakage function in FA varies between attack strategies, fault models, ciphers and countermeasure algorithms (unlike SCA leakage functions which are usually specified by Hamming weight/distance). For example, in a typical differential fault analysis attack, the leakage function is decided by the fault propagation path, which varies with the cipher, the fault location, and the countermeasure.

  2. A probabilistic program PP is a routine, which contains both probabilistic and deterministic assignments and variables, when represented in Single-Static-Assignment (SSA) form. A PP takes a joint distribution of input variables and outputs a joint distribution of output variables.

  3. Batch Normalization speeds up the learning process [68].

  4. The syntheses were performed using Synopsys Design Compiler and DFT Compiler (with STMicroelectronics CMOS65—a 65nm technology library due to STMicroelectronics). No area/timing optimization was imposed during synthesis. All Synopsys tools utilized in this work are under registered trademarks of Synopsys Inc (https://www.synopsys.com).

  5. There are 16 such locations.

  6. ALAFA requires the construction of all possible subsets up to the specific leakage-order. In the present case, we need to go up to order 128. The number of subsets up to order 128 is \(2^{128}\), which is infeasible to cover. So we consider one case where the order of test is 128. The result shown in the plots is for test order 128.

  7. Note that for this countermeasure, leakage has been observed while the ciphertexts are considered bit-wise.

  8. https://github.com/emsec/ImpeccableCircuitsII.

  9. https://github.com/thisimon/Friet.git.

  10. https://www.sourceware.org/gdb/.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful comments and suggestions for improving the paper. Debdeep Mukhopadhyay would also like to thank the Department of Science and Technology (DST), Govt of India, IHUB NTIHAC Foundation, C3i Building, IIT Kanpur, and Centre on HARDWARE-SECURITY ENTREPRENEURSHIP RESEARCH & DEVELOPMENT, Meity, India, for partially funding this work.

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Authors and Affiliations

  1. School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore

    Sayandeep Saha

  2. Center for Cyber Security, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

    Manaar Alam

  3. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Arnab Bag, Debdeep Mukhopadhyay & Pallab Dasgupta

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Saha, S., Alam, M., Bag, A. et al. Learn from Your Faults: Leakage Assessment in Fault Attacks Using Deep Learning. J Cryptol 36, 19 (2023). https://doi.org/10.1007/s00145-023-09462-6

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