A Dataset Generation Framework for profiling Disassembly attacks using Side-Channel Leakages and Deep Neural Networks

TL;DR

This paper introduces deep neural network models, including LSTM, to improve side-channel attack accuracy on low-resolution data by extracting features and predicting instruction flows, demonstrating significant performance gains.

Contribution

It presents novel deep learning models, including LSTM, for extracting features and predicting instruction flows from low-resolution side-channel traces, enhancing attack robustness.

Findings

Achieved 54.58% accuracy in instruction classification.

LSTM model reached 94.39% accuracy in cryptographic instruction prediction.

Outperformed existing methods on the proposed datasets.

Abstract

Various studies among side-channel attacks have tried to extract information through leakages from electronic devices to reach the instruction flow of some appliances. However, previous methods highly depend on the resolution of traced data. Obtaining low-noise traces is not always feasible in real attack scenarios. This study proposes two deep models to extract low and high-level features from side-channel traces and classify them to related instructions. We aim to evaluate the accuracy of a side-channel attack on low-resolution data with a more robust feature extractor thanks to neural networks. As inves-tigated, instruction flow in real programs is predictable and follows specific distributions. This leads to proposing a LSTM model to estimate these distributions, which could expedite the reverse engineering process and also raise the accuracy. The proposed model for leakage classification reaches 54.58% accuracy on average and outperforms other existing methods on our datasets. Also, LSTM model reaches 94.39% accuracy for instruction prediction on standard implementation of cryptographic algorithms.