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Title: Automatic characterization of exploitable faults : a machine learning approach
Authors: Dasgupta, Pallab
Saha, Sayandeep
Jap, Dirmanto
Patranabis, Sikhar
Mukhopadhyay, Debdeep
Bhasin, Shivam
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Block Cipher
Issue Date: 2019
Source: Saha, S., Jap, D., Patranabis, S., Mukhopadhyay, D., Bhasin, S., & Dasgupta, P. (2019). Automatic characterization of exploitable faults : a machine learning approach. IEEE Transactions on Information Forensics and Security, 14(4), 954-968. doi:10.1109/TIFS.2018.2868245
Series/Report no.: IEEE Transactions on Information Forensics and Security
Abstract: Characterizing the fault space of a cipher to filter out a set of faults potentially exploitable for fault attacks (FA), is a problem with immense practical value. A quantitative knowledge of the exploitable fault space is desirable in several applications, like security evaluation, cipher construction and implementation, design, and testing 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 strategy to solve this problem, which should be able to characterize each individual fault instance quickly. On the other hand, the automation strategy is expected to be applicable to most of the block cipher constructions. Existing techniques for automated 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. Experimental validation of this idea 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. Utilizing this observation, we propose a statistical framework for exploitable fault space characterization, which can provide an estimate of the success rate of an attacker corresponding to a given fault model and fault location. The framework also returns test vectors leading towards successful attacks. As a potential application, the effect of different S-Boxes on the fault space of a cipher is evaluated utilizing the framework.
ISSN: 1556-6013
DOI: 10.1109/TIFS.2018.2868245
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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