A Hybrid Attention Network for Malware Detection Based on Multi-Feature Aligned and Fusion

With the widespread use of computers, the amount of malware has increased exponentially. Since dynamic detection is costly in both time and resources, most existing malware detection methods are based on static features. However, existing static methods mainly rely on single feature types of malware, while few pay attention to multi-feature fusion. This paper presents a novel multi-feature extraction and fusion method to effectively detect malware variants by combining binary and opcode features. We propose a stacked convolutional network to capture the temporal and discontinuity information in the function call of the binary file from malware. Additionally, we adopt the triangular attention algorithm to extract code-level features from assembly code. Additionally, these two extracted features are aligned and fused by the cross-attention, which could provide a stable feature representation. We evaluate our method on two different datasets. It achieves an accuracy of 0.9954 on the Kaggle Malware Classification dataset and an accuracy of 0.9544 on a large real-world dataset. To optimize our detection model, we conduct in-depth discussions on different feature extractors and multi-feature fusion strategies. Moreover, a visualized attention module in our model is provided to explain its superiority in the opcode feature extraction. An experimental analysis is performed against five baseline deep learning models and five state-of-the-art malware detection models, which reveals that our strategy outperforms competing approaches in all evaluation circumstances.