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Malware Classification Based on Shallow Neural Network

Author

Listed:
  • Pin Yang

    (College of Cybersecurity, Sichuan University, Chengdu 610065, China)

  • Huiyu Zhou

    (College of Cybersecurity, Sichuan University, Chengdu 610065, China)

  • Yue Zhu

    (College of Cybersecurity, Sichuan University, Chengdu 610065, China)

  • Liang Liu

    (College of Cybersecurity, Sichuan University, Chengdu 610065, China)

  • Lei Zhang

    (College of Cybersecurity, Sichuan University, Chengdu 610065, China)

Abstract

The emergence of a large number of new malicious code poses a serious threat to network security, and most of them are derivative versions of existing malicious code. The classification of malicious code is helpful to analyze the evolutionary trend of malicious code families and trace the source of cybercrime. The existing methods of malware classification emphasize the depth of the neural network, which has the problems of a long training time and large computational cost. In this work, we propose the shallow neural network-based malware classifier (SNNMAC), a malware classification model based on shallow neural networks and static analysis. Our approach bridges the gap between precise but slow methods and fast but less precise methods in existing works. For each sample, we first generate n-grams from their opcode sequences of the binary file with a decompiler. An improved n-gram algorithm based on control transfer instructions is designed to reduce the n-gram dataset. Then, the SNNMAC exploits a shallow neural network, replacing the full connection layer and softmax with the average pooling layer and hierarchical softmax, to learn from the dataset and perform classification. We perform experiments on the Microsoft malware dataset. The evaluation result shows that the SNNMAC outperforms most of the related works with 99.21% classification precision and reduces the training time by more than half when compared with the methods using DNN (Deep Neural Networks).

Suggested Citation

  • Pin Yang & Huiyu Zhou & Yue Zhu & Liang Liu & Lei Zhang, 2020. "Malware Classification Based on Shallow Neural Network," Future Internet, MDPI, vol. 12(12), pages 1-17, December.
  • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:219-:d:455046
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    Cited by:

    1. Shangyi Yan & Jingya Wang & Zhiqiang Song, 2022. "Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling," Future Internet, MDPI, vol. 14(8), pages 1-19, July.

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