Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning

TL;DR

This paper introduces an iterative transfer learning approach for deep learning-based side-channel attacks, leveraging correlations between bytes to improve attack performance, especially with limited data.

Contribution

It presents a novel iterative transfer learning method that enhances side-channel attack models by exploiting inter-byte correlations, reducing data requirements.

Findings

Improves attack accuracy with limited data

Effective with thermal and power consumption maps

Applicable to multilayer perceptron and CNN models

Abstract

This paper proposes the use of iterative transfer learning applied to deep learning models for side-channel attacks. Currently, most of the side-channel attack methods train a model for each individual byte, without considering the correlation between bytes. However, since the models' parameters for attacking different bytes may be similar, we can leverage transfer learning, meaning that we first train the model for one of the key bytes, then use the trained model as a pretrained model for the remaining bytes. This technique can be applied iteratively, a process known as iterative transfer learning. Experimental results show that when using thermal or power consumption map images as input, and multilayer perceptron or convolutional neural network as the model, our method improves average performance, especially when the amount of data is insufficient.