Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection

Authors

  • Amjed Abbas Ahmed Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
  • Mohammad Kamrul Hasan Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
  • Shayla Islam Institute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, Malaysia
  • Azana Hafizah Mohd Aman Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
  • Nurhizam Safie Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia

DOI:

https://doi.org/10.5755/j02.eie.33995

Keywords:

Non-profile side-channel attack, AES, CNN

Abstract

Deep learning (DL) is a new option that has just been made available for side-channel analysis. DL approaches for profiled side-channel attacks (SCA) have dominated research till now. In this attack, the attacker has complete control over the profiling device and can collect many traces for a range of critical parameters to characterise device leakage before the attack. In this study, we apply DL algorithms to non-profiled data. An attacker can only retrieve a limited number of side-channel traces from a closed device with an unknown key value in non-profiled mode. The authors conducted this research. Key estimations and deep learning measurements can reveal the secret key. We prove that this is doable. This technology is excellent for non-profits. DL and neural networks can benefit these organisations. Neural networks can provide a new technique to verify the safety of hardware cryptographic algorithms. It was recently suggested. This study creates a non-profiled SCA utilising convolutional neural networks (CNNs) on an AVR microcontroller with 8 bits of memory and the AES-128 cryptographic algorithm. We used aligned power traces with several samples to demonstrate how challenging CNN-based SCA is in practise. This will help us reach our goals. Here is another technique to create a solid CNN data set. In particular, CNN-based SCA experiment data and noise effects are examined. These experiments employ power traces with Gaussian noise. The CNN-based SCA works well with our data set for non-profiled attacks. Gaussian noise on power traces causes many more issues. These results show that our method can recover more bytes successfully from SCA compared to other methods in correlation power analysis (CPA) and DL-SCA without regularisation.

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Published

2023-08-31

How to Cite

Ahmed, A. A., Hasan, M. K., Islam, S., Aman, A. H. M., & Safie, N. (2023). Design of Convolutional Neural Networks Architecture for Non-Profiled Side-Channel Attack Detection. Elektronika Ir Elektrotechnika, 29(4), 76-81. https://doi.org/10.5755/j02.eie.33995

Issue

Section

TELECOMMUNICATIONS ENGINEERING

Funding data