融合CNN-LSTM的硬件木马旁路检测方法

周康; 侯波; 王力纬; 雷登云; 罗永震; 黄中铠

doi:10.11999/JEIT250241

融合CNN-LSTM的硬件木马旁路检测方法

doi: 10.11999/JEIT250241 cstr: 32379.14.JEIT250241

周康^{1, 2},
侯波^2, ,,
王力纬²,
雷登云¹,
罗永震²,
黄中铠²

1.
广东工业大学集成电路学院广州 510006
2.
工业和信息化部电子第五研究所电子元器件可靠性物理及其应用技术重点实验室广州 510610

基金项目: 国家自然科学基金(62204062)，广东省自然科学基金(2023A1515011295)

详细信息

作者简介:
周康：男，硕士生，研究方向为硬件木马设计与检测

侯波：男，高级工程师，研究方向为集成电路硬件安全及可靠性

王力纬：男，研究员，研究方向为集成电路硬件安全及可靠性

雷登云：男，副教授，研究方向为数字集成电路设计及安全性分析

罗永震：男，工程师，研究方向为集成电路硬件安全及可靠性

黄中铠：男，工程师，研究方向为集成电路硬件安全及可靠性

通讯作者:
侯波　houbo@ceprei.com

中图分类号: TN710;TP18
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出版历程
- 收稿日期: 2025-04-07
- 修回日期: 2025-07-24
- 网络出版日期: 2025-08-05

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

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)

摘要

摘要: 随着集成电路设计与制造全球化，通过供应链植入硬件木马的潜在威胁日益显著。传统旁路检测方法依赖人工特征提取，易受噪声干扰且泛化能力不足，导致检测耗时且准确率不高。为此，该文提出一种基于一维卷积神经网络(CNN)及其与长短期记忆网络(LSTM)的组合架构(1D-CNN-LSTM)的硬件木马旁路检测方法，分别从局部空间特征与时序依赖关系两方面捕获硬件木马动态功耗信号特征，构建算法模型进行硬件木马检测。另外为了提高检测效率和算法鲁棒性，本文结合硬件木马特征对瞬态功耗原始数据进行预处理，并引入高斯噪声进行样本增强。以流片后的ASIC芯片为对象，开展硬件木马检测实验，结果显示经数据预处理后，1D-CNN-LSTM模型的训练效率提升近10倍，算法在四分类任务中的整体检测精度达到99.6%。论文所提出的方法可有效降低计算资源消耗、消除噪声干扰并实现高精度检测。
- 硬件木马 /
- 旁路检测 /
- 深度学习
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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图 1 最大池化和平均池化运算过程

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图 2 长短期记忆神经网络的内部结构

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图 3 本文检测流程图

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图 4 本文一维卷积神经网络结构图

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图 5 原始功耗数据和预处理后的功耗数据

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图 6 模型在测试集上的混淆矩阵

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表 1 训练集、测试集和验证集各类样本数量

	无木马	木马1	木马2	木马3
训练集	820	784	816	780
测试集	194	205	195	206
验证集	196	203	197	204

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表 2 1D-CNN-LSTM模型分类结果：各类别精确率、召回率与F1分数(%)

类别	精确率	召回率	F1值
原始芯片	100	99.5	99.7
木马1	100	100	100
木马2	99.0	100	99.5
木马3	99.5	99.0	99.2

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表 3 文献[16–19]所提方法与本文方法对比

	数据集	方法	检测准确率(%)	F1分数(%)
文献[16]	Trust-Hub上s444 基准测试电路	DANN	95.7	95.7
文献[17]	Trust-Hub上s9234测试电路仿真得到的电流信息	PCA-LSTM	98.0	99.0
文献[18]	IEEE数据集“Hardware Trojan Power & EM Side-Channel”，包含正常与木马样本，每个样本由2500个数据点的向量构成，包含功耗与电磁特征	Siamese CNN	86.8	87.1
		Siamese GRU	83.6	84.2
		Siamese CNN	73.5	74.3
文献[19]	基于 FPGA 的 AES 加密模块，在其上嵌入了4种不同类型的硬件木马，包含5类数据(包括无木马和4种不同木马)	CWT-改进 ConvNeXt	89.6	89.7
本文	定制ASIC无木马(原始芯片)以及木马芯片各40片，每一片木马芯片中包含3种木马电路，共包含4类旁路数据	CNN-LSTM	99.6	99.6

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参考文献(21)

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