Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor

Applied Sciences

Ashiq Anjum¹  Yinan Kong²  MohamadAli Mehrabi²  Naila Mukhtar² 

[1] Department of Computing and Mathematics, University of Derby, Derby DE22 1GB, UK;School of Engineering, Macquarie University, Sydney 2109, Australia;

关键词: side-channel analysis;    power-analysis attack;    embedded system security;    machine-learning classification;   

DOI  :  10.3390/app9010064

来源: DOAJ

【 摘 要 】

Security of embedded systems is the need of the hour. A mathematically secure algorithm runs on a cryptographic chip on these systems, but secret private data can be at risk due to side-channel leakage information. This research focuses on retrieving secret-key information, by performing machine-learning-based analysis on leaked power-consumption signals, from Field Programmable Gate Array (FPGA) implementation of the elliptic-curve algorithm captured from a Kintex-7 FPGA chip while the elliptic-curve cryptography (ECC) algorithm is running on it. This paper formalizes the methodology for preparing an input dataset for further analysis using machine-learning-based techniques to classify the secret-key bits. Research results reveal how pre-processing filters improve the classification accuracy in certain cases, and show how various signal properties can provide accurate secret classification with a smaller feature dataset. The results further show the parameter tuning and the amount of time required for building the machine-learning models.

【 授权许可】

Unknown