POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916869430050816 |
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| author | Mahfuz, Tanzim Paria, Sudipta Suha, Tasneem Bhunia, Swarup Chakraborty, Prabuddha |
| author_facet | Mahfuz, Tanzim Paria, Sudipta Suha, Tasneem Bhunia, Swarup Chakraborty, Prabuddha |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_22177 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage Mahfuz, Tanzim Paria, Sudipta Suha, Tasneem Bhunia, Swarup Chakraborty, Prabuddha Cryptography and Security 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. |
| title | POLARIS: Explainable Artificial Intelligence for Mitigating Power Side-Channel Leakage |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2507.22177 |