Cryptology ePrint Archive: Report 2017/1008

Automatic Characterization of Exploitable Faults: A Machine Learning Approach

Sayandeep Saha and Dirmanto Jap and Sikhar Patranabis and Debdeep Mukhopadhyay and Shivam Bhasin and Pallab Dasgupta

Abstract: Characterization of the fault space of a cipher to filter out a set of faults potentially exploitable for fault attacks (FA), is a prob- lem with immense practical value. A quantitative knowledge of the ex- ploitable fault space is desirable in several applications, like security evaluation, cipher construction and implementation, design, and test- ing of countermeasures etc. In this work, we investigate this problem in the context of block ciphers. The formidable size of the fault space of a block cipher mandates the use of an automation to solve this prob- lem, which should be able to characterize each individual fault instance quickly. On the other hand, the automation is expected to be applicable to most of the block cipher constructions. Existing techniques for au- tomated fault attacks do not satisfy both of these goals simultaneously and hence are not directly applicable in the context of exploitable fault characterization. In this paper, we present a supervised machine learning (ML) assisted automated framework, which successfully addresses both of the criteria mentioned. The key idea is to extrapolate the knowledge of some existing FAs on a cipher to rapidly figure out new attack instances on the same. Experimental validation of the proposed framework on two state-of-the-art block ciphers – PRESENT and LED, establishes that our approach is able to provide fairly good accuracy in identifying exploitable fault instances at a reasonable cost. Finally, the effect of different S-Boxes on the fault space of a cipher is evaluated utilizing the framework.

Category / Keywords: Security and Block Cipher and Fault Attack and Machine Learning

Date: received 10 Oct 2017, last revised 22 Nov 2017

Contact author: sayandeep iitkgp at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20171123:050424 (All versions of this report)

Short URL: ia.cr/2017/1008

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