Research on deep-learning-based techniques for advanced persistent threat malware detection and attribution

Nianfang Wang; Haiyan Fu

doi:10.1117/12.3029125

6 May 2024 Research on deep-learning-based techniques for advanced persistent threat malware detection and attribution

Nianfang Wang, Haiyan Fu

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131072D (2024) https://doi.org/10.1117/12.3029125
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

Advanced Persistent Threat attacks(APT) are targeted attacks launched by professional hacker organizations using advanced techniques, resulting in significant harm. Therefore, there is an urgent need to detect APT malware and trace their associated organizations. This paper proposes an improved Transformer-based method for APT malware detection and attribution. In terms of detection, dynamic behaviors of APT malware are extracted, and an information filtering gate mechanism is applied to reduce redundant feature noise in the original Transformer model. A contrastive learning constrained model is used for information filtering, self-training, and optimization. In terms of attribution, static features of APT malware samples are extracted, global features of sequence data are established using the Transformer model, local features are constructed using Incremental Dilated Convolutional Neural Network, and features are fused using attention mechanism. This method outperforms the baseline methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nianfang Wang and Haiyan Fu "Research on deep-learning-based techniques for advanced persistent threat malware detection and attribution", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131072D (6 May 2024); https://doi.org/10.1117/12.3029125

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KEYWORDS

Data modeling

Transformers

Machine learning

Performance modeling

Deep learning

Feature extraction

Statistical modeling

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