PATCH-FFT：使用基于补丁的频域变压器揭露潜伏的硬件木马
PATCH-FFT: Unmasking Dormant Hardware Trojans with Patch-Based Frequency-Domain Transformers
AI总结：
研究如何检测集成电路中潜伏的硬件木马，提出基于补丁的变压器架构，利用频域分析电源迹线，使用rFFT转换测量，实验显示该方法平均检测准确率达90.94%，优于现有方法，能检测休眠木马。
AI中文摘要：
恶意实体在集成电路中嵌入的硬件木马可以通过电源侧信道秘密泄露敏感信息，通常在休眠状态下未被检测到，直到特定触发条件激活其恶意行为。对于信息泄露木马，休眠状态下的检测至关重要。本文介绍了一种基于补丁的变压器架构，通过对电源迹线进行频域分析来检测休眠硬件木马。该方法使用实快速傅里叶变换（rFFT）将时域功率测量转换为频域表示，揭示传统时间序列分析中隐藏的频谱特征。实验结果表明，该方法在休眠和活跃木马场景中平均检测准确率达到90.94%，优于现有方法，尤其在检测先前时域方法无法处理的休眠木马方面。
英文摘要：
Hardware Trojans embedded by malicious entities in integrated circuits can covertly leak sensitive information through power side channels, often remaining undetected in their dormant state until specific trigger conditions activate their malicious behavior. For information-leaking Trojans, detection in the dormant state is critical, as once triggered, the secret data is already exfiltrated. This paper introduces a patch-based Transformer architecture for detecting dormant hardware Trojans through frequency-domain analysis of power traces. Our approach converts time-domain power measurements into frequency-domain representations using real Fast Fourier Transform (rFFT), revealing spectral signatures hidden in conventional time-series analysis. Experimental results demonstrate that our method achieves 90.94% average detection accuracy across dormant and active Trojan scenarios, outperforming state-of-the-art approaches particularly in detecting dormant Trojans that prior time-domain methods do not address.
