Cryptology ePrint Archive: Report 2017/673

Differential Fault Analysis Automation

Sayandeep Saha and Ujjawal Kumar and Debdeep Mukhopadhyay and Pallab Dasgupta

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.

Category / Keywords: Security and Block Cipher and Fault Attack and Data Mining

Original Publication (with minor differences): PROOFS : Security Proofs for Embedded Systems, 2017

Date: received 5 Jul 2017, last revised 11 Dec 2017

Contact author: sayandeep iitkgp at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20171211:145540 (All versions of this report)

Short URL: ia.cr/2017/673

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