Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN

Huang, Yuchen; Liu, Jingwen; Xiang, Xuanyi; Wen, Pan; Wen, Shiyuan; Chen, Yanru; Chen, Liangyin; Zhang, Yuanyuan

doi:10.3390/s24175518

Open AccessArticle

Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN

by

Yuchen Huang

^1,†

,

Jingwen Liu

^1,†

,

Xuanyi Xiang

²,

Pan Wen

¹,

Shiyuan Wen

³,

Yanru Chen

¹

,

Liangyin Chen

¹

and

Yuanyuan Zhang

^1,*

¹

The School of Computer Science, Sichuan University, Chengdu 610065, China

²

Pittsburgh Institute, Sichuan University, Chengdu 610065, China

³

College of Computer Science and Cybersecurity, Chengdu University of Technology, Chengdu 610059, China

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Sensors 2024, 24(17), 5518; https://doi.org/10.3390/s24175518

Submission received: 22 July 2024 / Revised: 13 August 2024 / Accepted: 24 August 2024 / Published: 26 August 2024

(This article belongs to the Special Issue AI Technology for Cybersecurity and IoT Applications)

Download

Browse Figures

Versions Notes

Abstract

Industrial Control Systems (ICSs) have faced a significant increase in malware threats since their integration with the Internet. However, existing machine learning-based malware identification methods are not specifically optimized for ICS environments, resulting in suboptimal identification performance. In this work, we propose an innovative method explicitly tailored for ICSs to enhance the performance of malware classifiers within these systems. Our method integrates the opcode2vec method based on preprocessed features with a conditional variational autoencoder–generative adversarial network, enabling classifiers based on Convolutional Neural Networks to identify malware more effectively and with some degree of increased stability and robustness. Extensive experiments validate the efficacy of our method, demonstrating the improved performance of malware classifiers in ICSs. Our method achieved an accuracy of 97.30%, precision of 92.34%, recall of 97.44%, and F1-score of 94.82%, which are the highest reported values in the experiment.

Keywords: industrial control system; cybersecurity; malware identification; machine learning

Share and Cite

MDPI and ACS Style

Huang, Y.; Liu, J.; Xiang, X.; Wen, P.; Wen, S.; Chen, Y.; Chen, L.; Zhang, Y. Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN. Sensors 2024, 24, 5518. https://doi.org/10.3390/s24175518

AMA Style

Huang Y, Liu J, Xiang X, Wen P, Wen S, Chen Y, Chen L, Zhang Y. Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN. Sensors. 2024; 24(17):5518. https://doi.org/10.3390/s24175518

Chicago/Turabian Style

Huang, Yuchen, Jingwen Liu, Xuanyi Xiang, Pan Wen, Shiyuan Wen, Yanru Chen, Liangyin Chen, and Yuanyuan Zhang. 2024. "Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN" Sensors 24, no. 17: 5518. https://doi.org/10.3390/s24175518

APA Style

Huang, Y., Liu, J., Xiang, X., Wen, P., Wen, S., Chen, Y., Chen, L., & Zhang, Y. (2024). Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN. Sensors, 24(17), 5518. https://doi.org/10.3390/s24175518

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Malware Identification Method in Industrial Control Systems Based on Opcode2vec and CVAE-GAN

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI