App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 4

Malware classification method based on image with information fusion and deep learning

Zhao Yonglin
Guo Chun
Lyu Xiaodan
Zhou Xuemei
State Key Laboratory of Public Big Data, College of Computer Science and Technology (Guizhou Institute of Confidentiality), Guizhou University, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0287

Abstract

Existing image-based malware classification methods enhance the accuracy of classification by extracting multiple pieces of information and characterizing malware using multi-channel images. However, in comparison to the utilization of single-channel images for the characterization of malware, using multi-channel images increases the complexity of the model and the computational load, while improving the classification accuracy. Therefore, this paper proposed a malware classification method based on image with information fusion and deep learning, called MCIIFD. This method allocates diverse information, including bytecode, opcode and operands, as well as data definition content, into disparate regions of a single-channel image. This incorporates some of the key information of malware while avoiding the high computational effort brought by multi-channel images. On the basis of this foundation, this paper designed a feature extraction and classification framework combining a convolutional neural network and a support vector machine. This enables the MCIIFD to achieve high classification accuracy via richer feature representations while maintaining low model complexity and high computational efficiency. The experimental results indicate that MCIIFD achieved an accuracy of 99.56% on the Big2015 dataset, validating its efficacy in malware classification tasks.

Foundation Support

国家自然科学基金资助项目(62162009)、贵州省重大科技专项项目(黔科合重大专项字[2024]014)、贵州省高等学校大数据与网络安全创新团队(黔教技[2023]052])

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0287
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 4

Publish History

[2025-12-12] Accepted Paper

Cite This Article

赵永林, 郭春, 吕晓丹, 等. 基于信息融合图像和深度学习的恶意软件分类方法 [J]. 计算机应用研究, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0287. (Zhao Yonglin, Guo Chun, Lyu Xiaodan, et al. Malware classification method based on image with information fusion and deep learning [J]. Application Research of Computers, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.07.0287. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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