Causality-Aware and Explainable Self-Supervised Spatio-Temporal Graph Learning for Hardware Trojan Detection

As hardware Trojans (HTs) are becoming increasingly stealthy in global semiconductor supply chains, the need for both robust and explainable detection methods is pressing. The use of deep learning models (e.g., Siamese networks, Transformer models) in side-channel signals has shown promising detection accuracy. Yet, they are black-box, data-intensive, and do not expose the causal, structural, and temporal relationships that indicate the presence of HTs. In this paper, we present a causality-focused and explainable detection framework that goes beyond pattern matching. We develop a Self-Supervised Spatio-Temporal Graph Neural Network (SST-GNN) that embeds spatio-temporal side-channel information. Our approach builds a graph that models gate-level components as nodes with temporal power and electromagnetic (EM) features, and functional and physical connections as edges. To address label scarcity, a common problem in real-world applications, we leverage a self-supervised pretraining approach. In particular, a context-aware contrastive loss allows the model to differentiate valid augmentations of benign subgraphs and their side-channel signatures, thus capturing general representations of benign components without Trojan labels. This involves a Causality-Aware GNN (CA-GNN) layer, which embeds differentiable causal discovery into graph learning. This process decouples correlation from causation, identifying the pathways potentially affected by HT trigger and payload. To explain decision making, a gradient-based graph explainer localizes minimal decisive subcircuits and pivotal time windows, generating intuitive detection reports. We evaluated our method on the IEEE Hardware Trojan Side-Channel Dataset (with netlist data), achieving state-of-the-art results (F1 > 0.98). In particular, the model achieves over 60% improvement in Trojan localization precision and false-positive rate, compared to Transformer-based approaches, with high label efficiency and adversarial robustness.