Abstract:

To address the inherent trade-off between detection accuracy and computational efficiency in intrusion detection systems (IDS) deployed under resource-constrained environments, this paper proposed a dual-branch intrusion detection method integrated with a lightweight attention mechanism. Motivated by the feature degradation commonly caused by model compression and the inter-class confusion among heterogeneous attack types, a collaborative Dual-Branch-DSC-ECA architecture was constructed: a dual-branch structure captures multi-scale features from network traffic to ensure comprehensive perception of complex attack behaviors; depthwise separable convolutions substantially reduced parameter count and computational complexity, while residual connections compensate for representational loss caused by depthwise separable decomposition; at the feature fusion stage, the efficient channel attention (ECA) mechanism adaptively recalibrates channel-wise feature responses to enhance the model’s sensitivity to anomalous traffic patterns. Experimental results on the UNSW-NB15 dataset show that the proposed method outperforms all baseline models, achieving 89.69% accuracy, 89.83% precision; and an F1-score of 89.70%—a 7.13-percentage-point improvement over the second-best baseline—reflecting a well-balanced trade-off between precision and recall. With only 0.043M parameters, comparable to the 1D CNN model, the proposed method demonstrates its suitability for deployment in resource-constrained network environments.

Key words: intrusion detection, deep learning, attention mechanism, depthwise separable convolution