Side-Channel Attack Counteraction via Machine LearningTargeted Power Compensation for Post-Silicon HW Security Patching

📋 论文概要

该论文提出了一种基于机器学习的目标性功率补偿方法，用于后硅硬件安全补丁，以对抗侧信道攻击。通过动态调整功率消耗来掩盖密码操作中的信息泄露，从而在不重新设计芯片的情况下提升安全性。

💡 主要创新点

• 提出了一种后硅硬件安全补丁方法，利用机器学习预测并补偿功率波动，无需修改原有设计。
• 实现了对侧信道攻击的实时防御，通过目标性功率补偿降低信息泄露风险。

🏷 关键词

📄 原文摘要

Southern University of Science and Technology, Shenzhen, China 1 2 *Equally Credited Authors (ECAs) Counteracting side-channel attacks has become a basic requirement in secure integrated circuits handling physical or sensitive data through cryptography, and preventing information leakage via power and electromagnetic (EM) emissions. Over time, the implementation of protection techniques against power analysis and EM attacks has progressively moved from design-specific (i.e., requiring redesign for their reuse [1],

👥 作者与机构

Qiang Fang*1, Longyang Lin*1,2, Yao Zu Wong1, Hui Zhang1, Massimo Alioto1

National University of Singapore, Singapore, Singapore