Next Article in Journal
Joint Stiffness Identification and Deformation Compensation of Serial Robots Based on Dual Quaternion Algebra
Next Article in Special Issue
Cache Misses and the Recovery of the Full AES 256 Key
Previous Article in Journal
Multi-Criteria Decision Making for Efficient Tiling Path Planning in a Tetris-Inspired Self-Reconfigurable Cleaning Robot
Previous Article in Special Issue
Improving Security and Reliability in Merkle Tree-Based Online Data Authentication with Leakage Resilience
Open AccessArticle

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

1
School of Engineering, Macquarie University, Sydney 2109, Australia
2
Department of Computing and Mathematics, University of Derby, Derby DE22 1GB, UK
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(1), 64; https://doi.org/10.3390/app9010064
Received: 6 November 2018 / Revised: 6 December 2018 / Accepted: 18 December 2018 / Published: 25 December 2018
(This article belongs to the Special Issue Side Channel Attacks)
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. View Full-Text
Keywords: side-channel analysis; power-analysis attack; embedded system security; machine-learning classification side-channel analysis; power-analysis attack; embedded system security; machine-learning classification
Show Figures

Figure 1

MDPI and ACS Style

Mukhtar, N.; Mehrabi, M.A.; Kong, Y.; Anjum, A. Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor. Appl. Sci. 2019, 9, 64. https://doi.org/10.3390/app9010064

AMA Style

Mukhtar N, Mehrabi MA, Kong Y, Anjum A. Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor. Applied Sciences. 2019; 9(1):64. https://doi.org/10.3390/app9010064

Chicago/Turabian Style

Mukhtar, Naila; Mehrabi, Mohamad A.; Kong, Yinan; Anjum, Ashiq. 2019. "Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor" Appl. Sci. 9, no. 1: 64. https://doi.org/10.3390/app9010064

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop