Research Article
Performance Analysis of Non-profiled Side Channel Attack Based on Multi-layer Perceptron Using Significant Hamming Weight Labeling
@INPROCEEDINGS{10.1007/978-3-031-08878-0_17, author={Ngoc-Tuan Do and Van-Phuc Hoang and Van Sang Doan}, title={Performance Analysis of Non-profiled Side Channel Attack Based on Multi-layer Perceptron Using Significant Hamming Weight Labeling}, proceedings={Industrial Networks and Intelligent Systems. 8th EAI International Conference, INISCOM 2022, Virtual Event, April 21--22, 2022, Proceedings}, proceedings_a={INISCOM}, year={2022}, month={6}, keywords={Hardware security Non-profile side channel attack Advanced Encryption Standard (AES) Multilayer perceptron (MLP) Imbalanced classes}, doi={10.1007/978-3-031-08878-0_17} }- Ngoc-Tuan Do
Van-Phuc Hoang
Van Sang Doan
Year: 2022
Performance Analysis of Non-profiled Side Channel Attack Based on Multi-layer Perceptron Using Significant Hamming Weight Labeling
INISCOM
Springer
DOI: 10.1007/978-3-031-08878-0_17
Abstract
Deep learning (DL) techniques have become popular for side-channel analysis (SCA) in the recent years. This paper proposes and evaluates the applications of multilayer perceptron (MLP) models for non-profiled attacks on the AES-128 encryption implementation in different scenarios, such as high dimensional data, imbalanced classes, and the impact of additive noise. Along with the designed models, a labeling technique called significant Hamming weight (SHW) and dataset reconstruction method are introduced for solving the imbalanced dataset problem. In addition, using SHW in the non-profiled context can reduce the number of measurements needed by approximately 30%. The experimental results show that the DL based SCA with our reconstructed dataset for different targets of ASCAD, RISC-V microcontroller has achieved a higher performance of non-profiled attacks. Comparing to the binary labeling technique, SHW labeling provides better results with the presence of the additive noise.
