Potential of Unsupervised Deep Learning for Detection of EM Side-Channel Attacks

The growing concern over side-channel emissions and their impact on the confidentiality of computer applications and systems has led to increased attention towards the vulner-ability of the backside of the Si substrate. This research paper proposes an innovative potential approach to bolster hardware security and mitigate vulnerabilities against electromagnetic side-channel attacks. The approach involves the fusion of Multi-physics simulation and deep learning techniques to accurately identify the most susceptible locations for side-channel leakage on the backside of silicon substrates within integrated circuits. By employing contextual feature extraction and an attention mechanism, the study aims to enhance chip-level security analysis and automate the detection of points of interest that are prone to side-channel attacks. The presents potential method overcomes limitations present in previous works, leading to improved quality and accuracy of the results.