BiTCN-CA: malicious code detection method based on bidirectional temporal convolution network and channel attention

Sicong Li; Jian Wang; Xiangke Guo

doi:10.1117/12.3012109

21 December 2023 BiTCN-CA: malicious code detection method based on bidirectional temporal convolution network and channel attention

Sicong Li, Jian Wang, Xiangke Guo

Author Affiliations +

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701J (2023) https://doi.org/10.1117/12.3012109
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

To cope with the escalating malicious code variants, we propose a malicious code classification method (BiTCN-CA) based on bidirectional temporal convolution network (BiTCN) and channel attention (Channel Attention (CA) based malicious code classification method (BiTCN-CA). The method fuses malicious code opcode and bytecode features to show different details; and builds BiTCN models to take advantage of the backward and forward dependencies of the features; and introduces the channel attention mechanism to further explore the deep dependencies within the malicious code data. The model is validated on kaggle dataset, and the experimental results show that the classification accuracy of malicious code based on BiTCN-CA can reach 99.36% with fast convergence speed and low classification error, and finally the effectiveness of the model is proved by comparison experiment and ablation experiment.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sicong Li, Jian Wang, and Xiangke Guo "BiTCN-CA: malicious code detection method based on bidirectional temporal convolution network and channel attention", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701J (21 December 2023); https://doi.org/10.1117/12.3012109

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KEYWORDS

Feature extraction

Convolution

Education and training

Performance modeling

Data modeling

Feature fusion

Machine learning

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