Cryptology ePrint Archive: Report 2020/717

Fault Location Identification By Machine Learning

Anubhab Baksi and Santanu Sarkar and Akhilesh Siddhanti and Ravi Anand and Anupam Chattopadhyay

Abstract: As the fault based analysis techniques are becoming more and more powerful, there is a need to streamline the existing tools for better accuracy and ease of use. In this regard, we propose a machine learning assisted tool that can be used in the context of a differential fault analysis. In particular, finding the exact fault location by analyzing the XORed output of a stream cipher/ stream cipher based design is somewhat non-trivial. Traditionally, Pearson's correlation coefficient is used for this purpose. We show that a machine learning method is more powerful than the existing correlation coefficient, aside from being simpler to implement. As a proof of concept, we take two variants of Grain-128a (namely a stream cipher, and a stream cipher with authentication), and demonstrate that machine learning can outperform correlation with the same training/testing data. Our analysis shows that the machine learning can be considered as a replacement for the correlation in the future research works.

Category / Keywords: secret-key cryptography / differential fault analysis, stream cipher, machine learning

Date: received 15 Jun 2020, last revised 16 Jun 2020

Contact author: anubhab001 at e ntu edu sg

Available format(s): PDF | BibTeX Citation

Version: 20200616:122223 (All versions of this report)

Short URL: ia.cr/2020/717


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