The Improving Side-Channel Attack Detection Through Attention-Based Mechanisms and Adversarial Training

Attention-Based Mechanisms and Adversarial Training

Abstract

Side-channel attacks (SCAs), which exploit physical attributes like electromagnetic emissions and power usage, pose significant threats to cryptographic systems. This research proposes a novel SCA detection method by integrating adversarial training with attention-based mechanisms to improve model accuracy and robustness. The model enhances detection by 52-fold, utilizing adversarial training to resist disrupted inputs and attention mechanisms to focus on critical signals. Experimental results demonstrate the system's capability to detect SCAs in real-time, achieving an accuracy of 94.8%, a precision of 91.2%, and robustness of 93.5%, making it a strong solution for cryptographic security.

OBJECTIVES: The primary objectives of this research are to develop a robust model for detecting side-channel attacks in cryptographic systems, improve detection accuracy through the use of adversarial training, and incorporate attention mechanisms to enhance the model's focus on critical signals for better threat identification.

METHODS: The proposed model employs adversarial training to enhance robustness against disrupted inputs and integrates attention-based mechanisms to prioritize significant signals from the data. This combination improves the system's ability to detect SCAs efficiently in real-time scenarios.

RESULTS: The experimental results show a significant improvement in detection, with a 52-fold increase in accuracy. The model achieves an overall accuracy of 94.8%, precision of 91.2%, and robustness of 93.5%, demonstrating its efficacy in identifying side-channel attacks.

CONCLUSION: This novel approach of combining adversarial training with attention mechanisms provides a powerful and efficient solution for real-time detection of side-channel attacks. The model's high accuracy, precision, and robustness make it a valuable tool for securing cryptographic systems from physical threats.