arXiv subjects

Mahsa Tahghigh

Publications and source records attributed to Mahsa Tahghigh.

3 recordsLinked to original sources

arXiv2026

Cross-Scale Persistence Analysis of EM Side-Channels for Reference-Free Detection of Always-On Hardware Trojans

Always-on hardware Trojans pose a serious challenge to integrated circuit trust, as they remain active during normal operation and are difficult to detect in post-deployment settings without trusted golden references. This paper presents a reference-free detection framework based on cross-scale persistence analysis of electromagnetic (EM) side-channels, targeting always-on parasitic hardware behavior. The proposed method analyzes EM emissions across multiple time-frequency resolutions and constructs stability maps that capture the consistency of spectral features over repeated executions. Gaussian Mixture Models (GMMs) with Bayesian Information Criterion (BIC) based model selection are used to characterize statistical structure at each scale. We introduce cross-scale saturation, variability, and median mixture complexity metrics that quantify whether statistical structure evolves naturally or remains persistently anchored across resolutions. Experimental results on AES implementations show that Trojan-free designs exhibit scale-dependent variability consistent with transient switching behavior, while always-on Trojans produce persistent statistical signatures that suppress cross-scale evolution. Furthermore, different Trojan classes, such as workload-correlated leakage-information Trojans and independent ring-oscillator Trojans, exhibit distinct persistence patterns. These findings demonstrate that cross-scale persistence provides a physically interpretable and robust assurance signal for unsupervised, reference-free detection of always-on hardware Trojans.

cs.CR↗

arXiv2026

Reference-Free EM Validation Flow for Detecting Triggered Hardware Trojans

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.

cs.CR↗

arXiv2026

Reference-Free Spectral Analysis of EM Side-Channels for Always-on Hardware Trojan Detection

Always-on hardware Trojans (HTs) pose a critical risk to trusted microelectronics, yet most side-channel detection methods rely on unavailable golden references. We present a reference-free approach that combines time-frequency EM analysis with Gaussian Mixture Models (GMMs). By applying Short-Time Fourier Transform (STFT) at multiple window sizes, we show that HT-free circuits exhibit fluctuating statistical structure, while always-on HTs leave persistent footprints with fewer, more consistent mixture components. Results on AES-128 demonstrate feasibility without requiring reference models.

cs.CR↗