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<p>Ensuring the security of integrated circuits (ICs) requires reliable detection and precise localization of Hardware Trojans (HTs), which remain challenging due to increasing design complexity and lack of golden references. This paper introduces a machine learning framework that integrates graph-based modeling, Graph Neural Networks (GNNs), and nearest neighbour (NN) enhancement for fast and accurate HT detection at the gate-level netlist without relying on the golden reference. Three different machine learning models are employed in the present work. Case-I uses a decision tree classifier with Principal Component Analysis (PCA) for binary detection of Trojan presence, as a reference model. The decision tree based machine learning model is initially validated against the formal verification method. The decision tree model is only able to identify HT presence without localizing the locations of HT in the circuit. Case-II uses a GNN-based graph-to-graph classification, distinguishing clean netlists from the infected one at sub-graph level (coarse grained). The model is able to map the infected sub-graphs back to the initial netlist circuit for the pinpointing of HTs at sub-graph level. Case-III uses a further simplified model with GNN-based node classification, enabling fine-grained localization of compromised gates in the circuit using only nodes. This model is ideal for pinpointing the exact Trojan locations within large-scale circuits. Subsequently, NN based concept is embedded with GNN models for further enhancing the detection accuracy of Case-II (accuracy improved from 62.8% to 97.7%) and Case-III (accuracy improved from 79.8% to 97.7%). Also, the scalability of the proposed approaches across diverse Trojan types, including combinational, sequential, and state-triggered attacks are validated by experiments carried on Trust-Hub benchmarks and Yosys-generated datasets. Comparative evaluation with state-of-the-art methods demonstrates superior performance, achieving 98.5% precision, 99.1% recall, and 96.7% F1-score, while maintaining computational efficiency. By combining graph structural learning with NN-based contextual refinement, the proposed work delivers a high-performance, architecture-agnostic solution for detecting and localizing hardware Trojans in modern ICs as tested against unknown designs.</p>

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Fast and accurate identification of hardware Trojan locations in gate-level netlist using nearest neighbour approach integrated with machine learning technique

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Anindita Chattopadhyay,
  • Siddharth Bisariya,
  • Vijay Kumar Sutrakar

摘要

Ensuring the security of integrated circuits (ICs) requires reliable detection and precise localization of Hardware Trojans (HTs), which remain challenging due to increasing design complexity and lack of golden references. This paper introduces a machine learning framework that integrates graph-based modeling, Graph Neural Networks (GNNs), and nearest neighbour (NN) enhancement for fast and accurate HT detection at the gate-level netlist without relying on the golden reference. Three different machine learning models are employed in the present work. Case-I uses a decision tree classifier with Principal Component Analysis (PCA) for binary detection of Trojan presence, as a reference model. The decision tree based machine learning model is initially validated against the formal verification method. The decision tree model is only able to identify HT presence without localizing the locations of HT in the circuit. Case-II uses a GNN-based graph-to-graph classification, distinguishing clean netlists from the infected one at sub-graph level (coarse grained). The model is able to map the infected sub-graphs back to the initial netlist circuit for the pinpointing of HTs at sub-graph level. Case-III uses a further simplified model with GNN-based node classification, enabling fine-grained localization of compromised gates in the circuit using only nodes. This model is ideal for pinpointing the exact Trojan locations within large-scale circuits. Subsequently, NN based concept is embedded with GNN models for further enhancing the detection accuracy of Case-II (accuracy improved from 62.8% to 97.7%) and Case-III (accuracy improved from 79.8% to 97.7%). Also, the scalability of the proposed approaches across diverse Trojan types, including combinational, sequential, and state-triggered attacks are validated by experiments carried on Trust-Hub benchmarks and Yosys-generated datasets. Comparative evaluation with state-of-the-art methods demonstrates superior performance, achieving 98.5% precision, 99.1% recall, and 96.7% F1-score, while maintaining computational efficiency. By combining graph structural learning with NN-based contextual refinement, the proposed work delivers a high-performance, architecture-agnostic solution for detecting and localizing hardware Trojans in modern ICs as tested against unknown designs.