SVM-based malware classification through deep learning method

Jiawei Shi; Wancheng Wang; Meng Zhang; Qinbo Yao; Shiyuan Cheng; Zhifei Zhan

doi:10.1117/12.3054107

3 January 2025 SVM-based malware classification through deep learning method

Jiawei Shi, Wancheng Wang, Meng Zhang, Qinbo Yao, Shiyuan Cheng, Zhifei Zhan

Author Affiliations +

Proceedings Volume 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024); 1344206 (2025) https://doi.org/10.1117/12.3054107
Event: Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 2024, Kaifeng, China

Abstract

Various daily applications, including image categorization, natural language comprehension, and voice identification, heavily rely on fully connected and convolutional neural networks. When tackling classification problems, conventional deep learning architectures typically employ softmax activation functions to normalize outputs and minimize the model's cross-entropy loss. In this study, we present a novel malware classification model that integrates hybrid support vector machines with neural networks. Notably, this model substitutes the conventional softmax layer in neural networks with a support vector machine, thereby shifting the learning process towards minimizing margin losses instead of cross-entropy losses. This modification enhances the precision of malware classification for both standard machine learning algorithms and several prevalent deep learning models. To validate our proposed model, we employed the Malimg dataset, comprising malware images derived from binary malware samples. Subsequently, we trained an DL-SVM model to assign scores to each distinct malware family. Experimental outcomes revealed that the malware classification model achieved an accuracy of 96.31%, a precision rate of 97.00%, a recall rate of 97.35%, and an F1 score of 97.16%, respectively.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiawei Shi, Wancheng Wang, Meng Zhang, Qinbo Yao, Shiyuan Cheng, and Zhifei Zhan "SVM-based malware classification through deep learning method", Proc. SPIE 13442, Fifth International Conference on Signal Processing and Computer Science (SPCS 2024), 1344206 (3 January 2025); https://doi.org/10.1117/12.3054107

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