A Railway Mobile Terminal Malware Detection Method Based on SE-ResNet

This paper proposes a residual network model integrated with an attention mechanism module for the detection and classification of malware on railway mobile terminals. To address the issues of insufficient and imbalanced samples, Wasserstein Generative Adversarial Networks (WGANs) are utilized to synthesize grayscale image data of malware with high similarity to real samples. The performance of the model is evaluated on the publicly available CIC-InvesAndMal2019 dataset and an extended balanced dataset. Experimental results demonstrate that the synergistic integration of residual networks, WGANs, and attention mechanisms significantly enhances the performance of the malware detection model. In the context of railway applications, the proposed approach also achieves favorable classification performance when applied to image datasets derived from malware samples of railway mobile terminals. Multiple ablation studies are conducted to individually validate the contributions of each technical component in improving the classification model’s efficacy. The adoption of the SE-ResNet architecture combined with WGAN-based data augmentation presents a practical and efficient technical solution.