International Association for Cryptologic Research (IACR)
Abstract
Characterization of all possible faults in a cryptosystem exploitable for fault attacks is a problem
which is of both theoretical and practical interest for the cryptographic community. The complete
knowledge of exploitable fault space is desirable while designing optimal countermeasures for any
given crypto-implementation. In this paper, we address the exploitable fault characterization problem
in the context of Differential Fault Analysis (DFA) attacks on block ciphers. The formidable size
of the fault spaces demands an automated albeit fast mechanism for verifying each individual fault
instance and neither the traditional, cipher-specific, manual DFA techniques nor the generic and au-
tomated Algebraic Fault Attacks (AFA) [10] fulfill these criteria. Further, the diversified structures
of different block ciphers suggest that such an automation should be equally applicable to any block
cipher. This work presents an automated framework for DFA identification, fulfilling all aforemen-
tioned criteria, which, instead of performing the attack just estimates the attack complexity for each
individual fault instance. A generic and extendable data-mining assisted dynamic analysis frame-
work capable of capturing a large class of DFA distinguishers is devised, along with a graph-based
complexity analysis scheme. The framework significantly outperforms another recently proposed
one [6], in terms of attack class coverage and automation effort. Experimental evaluation on AES and
PRESENT establishes the effectiveness of the proposed framework in detecting most of the known
DFAs, which eventually enables the characterization of the exploitable fault space