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6 June 2024 Research on malicious code detection and classification based on dynamic and static features
Yueyang Shang, Fuwei Wang, Yunfei Zhang, Dong Li, Wenbin Tan
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Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 131750C (2024) https://doi.org/10.1117/12.3031906
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China
Abstract
Malicious code can reflect its malicious behavior through dynamic API sequences and static PE header information, and deep learning algorithms have made progress in such malicious code detection. The article designs a 1D-CNN-BiGRU network model based on one-dimensional convolutional neural networks and bidirectional gated recurrent neural networks. The model takes API call sequences and PE header information as feature inputs and undergoes convolutional computation and recurrent neural network learning training to further learn the features of malicious code. Through the analysis of experimental results, the correctness of the malicious code verification of this model is demonstrated. The detection accuracy of normal samples on dynamic API call sequences is over 97%, and the accuracy on static PE structures is 95.64%. It has good performance in malicious code detection.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yueyang Shang, Fuwei Wang, Yunfei Zhang, Dong Li, and Wenbin Tan "Research on malicious code detection and classification based on dynamic and static features", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 131750C (6 June 2024); https://doi.org/10.1117/12.3031906
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KEYWORDS
Data modeling
Feature extraction
Machine learning
Neural networks
Deep learning
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