Deep Side-Channel Attack Method Integrating Convolutional Block Attention Mechanism and Triplet Metric Learning

XU Yang; LI Kaibin; HE Xingxing

doi:10.11999/JEIT260140

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XU Yang, LI Kaibin, HE Xingxing. Deep Side-Channel Attack Method Integrating Convolutional Block Attention Mechanism and Triplet Metric Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260140

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XU Yang, LI Kaibin, HE Xingxing. Deep Side-Channel Attack Method Integrating Convolutional Block Attention Mechanism and Triplet Metric Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260140

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Deep Side-Channel Attack Method Integrating Convolutional Block Attention Mechanism and Triplet Metric Learning

doi: 10.11999/JEIT260140 cstr: 32379.14.JEIT260140

XU Yang¹,
LI Kaibin^{1
,
,},
HE Xingxing²

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
2.
School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China

Funds: Central Government Guided Local Development Fund (2025ZYDF075), The Fundamental Research Funds for Central Universities (2682024ZTPY041, 2682025ZTPY009), The Science and Technology Planning Project of Sichuan Province (2024YFHZ0316), Chengdu Soft Science Research Project (2026-RK00-00028-ZF)

Accepted Date: 2026-07-01
Rev Recd Date: 2026-07-01

Available Online: 2026-07-13

Abstract

Abstract

Objective Side-channel attack (SCA) constitutes one of the primary threats to the physical security of cryptographic chips, and leveraging deep learning techniques for key recovery has emerged as a research hotspot in the field of side-channel attack. However, existing deep analysis methods lack the ability to focus on key leakage intervals during feature extraction, especially in long-waveform and high-dimensional noise scenarios. They are easily disturbed by irrelevant background noise, leading to low feature extraction efficiency and slow guessing entropy convergence. To address these issues, this study proposes a deep side-channel analysis method integrating the Convolutional Block Attention Module (CBAM) and triplet loss, aiming to enhance the model's ability to capture weak leakage features in complex noise environments and improve key recovery efficiency. Methods The proposed method introduces CBAM into the convolutional neural network (CNN) to construct an adaptive feature extraction network. CBAM includes two sub-modules: Channel Attention Module (CAM) and Spatial Attention Module (SAM). CAM adaptively adjusts the weights of different feature channels to emphasize channels containing high signal-to-noise ratio (SNR) leakage information, while SAM locates key Points of Interest (POI) in the time domain to suppress background noise in non-leakage intervals. After feature calibration by CBAM, triplet loss is adopted as the optimization objective to constrain the distribution of embedded features, forcing similar samples to form compact clusters and different samples to maintain sufficient separation in the feature space. Finally, a multivariate Gaussian template attack is implemented using the optimized embedded features to recover the key. The overall framework is shown in (Fig.2). Results and Discussions Experiments are conducted on two public benchmark datasets (ASCAD and AES_HD) with guessing entropy (GE) and the minimum number of traces required for GE convergence to 1 ($ {T}_{GE0} $) as evaluation metrics. On the ASCAD dataset: (1) In the ASCAD_f (HW) scenario, the proposed method only needs 144 traces to recover the key, reducing by 51.0% compared with the traditional CNN model; (2) In the ASCAD_f (ID) scenario, merely 61 traces are required, a reduction of 68.0% from the traditional CNN; (3) In the random key setting (ASCAD_r), the method achieves convergence with 176 (HW) and 137 (ID) traces respectively, outperforming mainstream methods such as RL-SCA and Metric Learning (Table 2, Fig.3). On the low-SNR AES_HD dataset, the method reduces to 1219 traces, which is lower than MHA and NLS, and the guessing entropy converges smoothly without obvious fluctuations (Table 2, Fig.4). Furthermore, the desynchronization experiments demonstrate that even under strong desynchronization noise interference, the proposed method can still adaptively lock onto effective leakage moments, exhibiting excellent robustness against time-domain jitter(Table 3). Ablation studies further confirm the positive synergistic effect of the proposed architecture and thoroughly validate the rationality of core components (Table 4). Conclusions This study proposes an effective deep side-channel analysis method by integrating the CBAM attention mechanism and metric learning. The CBAM module enables the network to actively focus on key leakage information, solving the problem of insufficient focusing ability in traditional CNN-based methods. The triplet loss optimizes the discriminability of embedded features, further improving the accuracy of template matching. Experimental results on ASCAD and AES_HD datasets demonstrate that the method significantly reduces the number of traces required for key recovery and accelerates the convergence of guessing entropy, outperforming existing mainstream methods in both fixed and random key scenarios, as well as in low-SNR environments. Future work will focus on improving the adaptability of the model in scenarios with more severe synchronization disturbances, and enhancing its generalization ability in small sample scenarios.
- Side-channel attacks,
- Deep learning,
- CBAM,
- Triplet loss,
- Hardware security

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References(33)

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