POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage

TL;DR

POLARIS introduces an explainable AI-based framework that effectively reduces power side-channel leakage in microelectronic systems, outperforming existing solutions in leakage mitigation, efficiency, and overhead management.

Contribution

The paper presents POLARIS, a novel XAI-guided masking framework that automatically creates tailored training data for effective power leakage mitigation.

Findings

POLARIS outperforms VALIANT in leakage reduction.

POLARIS reduces execution time and overhead.

POLARIS effectively mitigates power side-channel attacks.

Abstract

Microelectronic systems are widely used in many sensitive applications (e.g., manufacturing, energy, defense). These systems increasingly handle sensitive data (e.g., encryption key) and are vulnerable to diverse threats, such as, power side-channel attacks, which infer sensitive data through dynamic power profile. In this paper, we present a novel framework, POLARIS for mitigating power side channel leakage using an Explainable Artificial Intelligence (XAI) guided masking approach. POLARIS uses an unsupervised process to automatically build a tailored training dataset and utilize it to train a masking model.The POLARIS framework outperforms state-of-the-art mitigation solutions (e.g., VALIANT) in terms of leakage reduction, execution time, and overhead across large designs.