Power Distribution Network Capacitive Decoupling for Side-Channel Resistance

R. Selvam, A. Tyagi
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引用次数: 3

Abstract

In power side-channel attacks (SCA), the adversary observes the power leakage at the external power pin to reverse engineer the secrets embedded in sensitive circuits. Mitigation techniques are often integrated into the logic to make power consumption data independent. In this paper, we develop a new design strategy for designing the on-chip power distribution network using a decoupling capacitance to thwart the power side-channel attack. The decoupling capacitances are introduced along the power lanes in a distributed fashion, to suppress the data-leakage from the sensitive-circuit. To facilitate the computer-aided design of such PDNs, we also develop approximate heuristics to extract feature vectors from the current (I) profile of the internal logic blocks, and for feature vector propagation over the on-chip power distribution network. We study the sensitivity of decoupling capacitance value and its placement over the power distribution network. Finally, we evaluate the side-channel resistance with and without decoupling capacitance using Spice level simulations. Machine Learning (ML) classifiers are used to quantify the side-channel strength in terms of success rate for power side-channel adversary. A 100pf decoupling capacitance, in some cases, reduces the ML success rate from 80% to 21% to provide significant SCA resistance.
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配电网侧道电阻电容解耦
在功率侧信道攻击(SCA)中,攻击者通过观察外部电源引脚处的功率泄漏来逆向工程嵌入敏感电路中的秘密。缓解技术通常集成到逻辑中,使功耗数据独立。在本文中,我们开发了一种新的设计策略,利用去耦电容来设计片上配电网络,以阻止功率侧信道攻击。去耦电容沿功率通道以分布式方式引入,以抑制敏感电路的数据泄漏。为了便于此类pdn的计算机辅助设计,我们还开发了近似启发式方法,从内部逻辑块的电流(I)轮廓中提取特征向量,并在片上配电网络上传播特征向量。研究了解耦电容值的灵敏度及其在配电网上的位置。最后,我们使用Spice级模拟评估了有无去耦电容的侧通道电阻。机器学习(ML)分类器用于根据功率侧信道对手的成功率来量化侧信道强度。在某些情况下,100pf去耦电容可将ML成功率从80%降低到21%,从而提供显著的SCA电阻。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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