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

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 sidechannel attacks, which infer sensitive data through dynamic...

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Vydané v:2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autori: Mahfuz, Tanzim, Paria, Sudipta, Suha, Tasneem, Bhunia, Swarup, Chakraborty, Prabuddha
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 22.06.2025
Predmet:
Explainable AI
Explainable Artificial Intelligence
Hardware Security
Manufacturing
Microelectronics
Performance gain
Power side-channel Analysis and Mitigation
Prevention and mitigation
Runtime
Scalability
Synthetic data
System-on-chip
Training
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Shrnutí: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 sidechannel 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.
DOI:10.1109/DAC63849.2025.11132622