An Automated Framework for Exploitable Fault Identification in Block Ciphers - A Data Mining Approach

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 automated 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 aforementioned 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 framework 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.