A CNN-LSTM Fusion-Based Method for Detecting Hardware Trojan Bypasses

ZHOU Kang; HOU Bo; WANG Liwei; LEI Dengyun; LUO Yongzhen; HUANG Zhongkai

doi:10.11999/JEIT250241

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ZHOU Kang, HOU Bo, WANG Liwei, LEI Dengyun, LUO Yongzhen, HUANG Zhongkai. A CNN-LSTM Fusion-Based Method for Detecting Hardware Trojan Bypasses[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250241

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ZHOU Kang, HOU Bo, WANG Liwei, LEI Dengyun, LUO Yongzhen, HUANG Zhongkai. A CNN-LSTM Fusion-Based Method for Detecting Hardware Trojan Bypasses[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250241

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A CNN-LSTM Fusion-Based Method for Detecting Hardware Trojan Bypasses

doi: 10.11999/JEIT250241 cstr: 32379.14.JEIT250241

1.
School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China
2.
Key Laboratory of Electronic Component Reliability Physics and Application Technology, The Fifth Electronics Research Institute of the Ministry of Industry and Information Technology, Guangzhou 510610, China

Funds: The National Natural Science Foundation of China (62204062), GuangDong Basic and Applied Basic Research Foundation (2023A1515011295)

Received Date: 2025-04-07
Rev Recd Date: 2025-07-24

Available Online: 2025-08-05

Abstract

Abstract

Objective The globalization of Integrated Circuit (IC) design and increasing reliance on outsourcing have heightened the vulnerability of hardware supply chains to malicious modifications, such as hardware Trojans. These covert circuits may remain dormant until triggered, causing data leakage, system performance degradation, or physical damage. Detecting such threats is essential for safeguarding the security and reliability of semiconductor devices. Traditional side-channel detection methods based on power consumption or timing analysis often depend on manually designed features, which are sensitive to noise and lack generalizability across hardware platforms. Therefore, these techniques suffer from low detection accuracy and high false-positive rates under practical conditions. To address these limitations, this study proposes a deep learning-based side-channel detection method. By leveraging the ability of neural networks to automatically extract features from raw power signals, the proposed approach targets the identification of subtle anomalies associated with Trojan activation. The aim is to develop a robust, scalable detection solution applicable to real-world industrial scenarios. Methods The proposed detection framework integrates a hybrid deep learning architecture that combines a One-Dimensional Convolutional Neural Network (1D-CNN) with a Long Short-Term Memory (LSTM) network (Fig. 5). This architecture is designed to exploit the complementary advantages of CNNs and LSTMs for feature extraction. Specifically, the 1D-CNN component captures local spatial correlations within transient power traces, which are critical for detecting short-term fluctuations indicative of Trojan activity. The convolutional filters automatically learn edges, patterns, and shifts in signal magnitude, thereby reducing reliance on manual feature engineering. In parallel, the LSTM component is employed to model long-range temporal dependencies in the power signal sequence. Compared with conventional Recurrent Neural Networks (RNNs), LSTMs incorporate memory gates that enable selective retention or dismissal of past information, making them suitable for analyzing time-series data such as power traces. This enhances the framework’s ability to detect sequential patterns and context-dependent anomalies that may emerge over extended periods. The dataset comprises real-world transient power traces collected from fabricated Application-Specific Integrated Circuit (ASIC) chips, including both Trojan-free and Trojan-infected samples. Each power trace contains 125,000 sample points, capturing high-resolution dynamic power consumption under controlled activation scenarios. To reduce computational complexity and focus the model on signal segments most relevant to Trojan detection, a preprocessing step is applied. Specifically, windows of power data are extracted around the rising edges of the clock signal, where circuit state transitions are most likely to reveal Trojan-induced anomalies. This reduces the data dimensionality to 22,485 points per sample. To enhance the robustness of the model and mitigate overfitting, Gaussian noise is injected into the training data for data augmentation. This simulates realistic environmental and sensor-related noise conditions. The final dataset is divided into training, validation, and test sets in a 50%-25%-25% ratio, with balanced distributions of Trojan-free and Trojan-infected samples. Results and Discussions The experimental evaluation confirms the effectiveness of the proposed hybrid deep learning model for accurate and efficient hardware Trojan detection. By applying preprocessing to reduce input dimensionality, the training time is reduced by approximately an order of magnitude, substantially lowering computational requirements without compromising detection accuracy. The final model, trained using the RMSProp optimizer with a learning rate of 0.0005 and a batch size of 64, achieves a detection accuracy of 99.6% for the four-class classification task (Table 1). Analysis of the confusion matrix (Fig. 6) demonstrates that the model reliably distinguishes Trojan-free samples from three different types of Trojan-infected samples. Precision and recall rates exceed 99% across all classes, with minimal misclassification. The introduction of Gaussian noise during training further enhances the model’s generalization ability, ensuring stable performance on previously unseen test data. The macro-average F1-score reaches 99.6%, indicating balanced detection performance for all classes. In comparative evaluations with existing state-of-the-art methods, including Domain-Adversarial Neural Networks (DANN), Principal Component Analysis combined with LSTM (PCA-LSTM), and Siamese networks (Table 3), the proposed 1D-CNN-LSTM model consistently achieves superior accuracy and robustness. A key advantage is the model’s ability to process real-world measured power traces, rather than relying solely on simulated data. These results highlight the significance of combining spatial and temporal modeling for side-channel analysis and demonstrate the potential of deep learning techniques for hardware security applications. Nevertheless, the current experiments are conducted under ideal laboratory conditions with controlled data acquisition. Practical deployments are likely to encounter additional challenges, such as environmental fluctuations, measurement noise, and potential adversarial interference with power signals. Addressing these limitations remains an open research problem. Conclusions This paper proposes a deep learning-based hardware Trojan side-channel detection method that integrates a 1D-CNN-LSTM hybrid model to automatically extract and analyze features from power consumption signals. The method achieves substantial improvements in both detection efficiency and accuracy, supporting the feasibility of deep learning for hardware security applications. Future research will focus on addressing real-world challenges, including sensor noise, environmental variability, and adversarial attacks, as well as exploring semi-supervised or unsupervised learning to reduce reliance on labeled data. These findings provide a promising basis for enhancing the security and reliability of IC designs against hardware Trojan threats.
- Hardware Trojan,
- Side-channel detection,
- Deep learning

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References(21)

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