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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