Résumé: Fault injection-based cryptanalysis is one of the most powerful practical threats to modern cryptographic primitives. Popular countermeasures to such fault-based attacks generally use some form of redundant computation to detect and react/correct the injected faults. However, such countermeasures are shown to be vulnerable to selective fault injections. In this article, we aim to develop a cryptographic primitive that is fault tolerant by its construction and does not require to compute the same value multiple times. We utilize the effectiveness of Neural Networks (NNs), which show "some degree" of robustness by functioning correctly even after the occurrence of faults in any of its parameters. We also propose a novel strategy that enhances the fault tolerance of the implementation to "high degree" (close to 100%) by incorporating selective constraints in the NN parameters during the training phase. We evaluated the performance of revised NN considering both software and FPGA implementations for standard cryptographic primitives like 8×8 AES SBox and 4×4 PRESENT SBox. The results show that the fault tolerance of such implementations can be significantly increased with the proposed methodology. Such NN-based cryptographic primitives will provide inherent resistance against fault injections without requiring any redundancy countermeasures.

Langue: Anglais