Hardware Trojans (HTs) threaten the trust and reliability of integrated circuits (ICs), particularly when triggered HTs remain dormant during standard testing and activate only under rare conditions. Existing electromagnetic (EM) side-channel-based detection techniques often rely on golden references or labeled data, which are infeasible in modern distributed manufacturing. This paper introduces a reference-free, design-agnostic framework for detecting triggered HTs directly from post-silicon EM emissions. The proposed flow converts each EM trace into a time-frequency scalogram using Continuous Wavelet Transform (CWT), extracts discriminative features through a convolutional neural network (CNN), reduces dimensionality with principal component analysis (PCA), and applies Bayesian Gaussian Mixture Modeling (BGMM) for unsupervised probabilistic clustering. The framework quantifies detection confidence using posterior-based metrics (alpha_{post}, beta_{post}), Bayesian information criterion (Delta BIC), and Mahalanobis cluster separation (D), enabling interpretable anomaly decisions without golden data. Experimental validation on AES-128 designs embedded with four different HTs demonstrates high separability between HT-free and HT-activated conditions and robustness to PCA variance thresholds. The results highlight the method's scalability, statistical interpretability, and potential for extension to runtime and in-field HT monitoring in trusted microelectronics.

翻译：硬件木马（HTs）对集成电路（ICs）的可信性与可靠性构成严重威胁，特别是触发型硬件木马在标准测试期间保持休眠状态，仅在罕见条件下激活。现有的基于电磁（EM）侧信道的检测技术通常依赖于黄金参考数据或标注数据，这在现代分布式制造中难以实现。本文提出了一种无参考、与设计无关的框架，用于直接从硅后电磁辐射中检测触发型硬件木马。所提出的流程通过连续小波变换（CWT）将每条电磁迹线转换为时频尺度图，利用卷积神经网络（CNN）提取判别性特征，通过主成分分析（PCA）进行降维，并应用贝叶斯高斯混合建模（BGMM）进行无监督概率聚类。该框架使用后验度量（α_{post}、β_{post}）、贝叶斯信息准则（ΔBIC）和马哈拉诺比斯聚类分离度（D）量化检测置信度，从而在无需黄金数据的情况下实现可解释的异常决策。在嵌入了四种不同硬件木马的AES-128设计上进行的实验验证表明，无木马状态与木马激活状态之间具有高度可分离性，且对PCA方差阈值具有鲁棒性。结果凸显了该方法在可信微电子领域内，对于运行时及现场硬件木马监测的可扩展性、统计可解释性以及扩展潜力。