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The number of malware increased rapidly, and malware classification and detection techniques were facing serious challenges. To address this issue, a new malware classification and detection framework, MGFG(malware gray image Fourier transform gist) model was proposed. The malware PE files were converted into gray images, and then the two-dimensional discrete Fourier transform was applied to the gray images of malware to obtain their spectrograms. By processing the spectrogram frequencies, the effect of malware image denoising was achieved. Finally, the global features(gist) were extracted to detect and classify malware. The experimental results showed that the MGFG model had better robustness and higher classification accuracy on multiple datasets for the classification problem of shelled, obfuscated malware.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2023161
China Classification Code:TP311.5;TP309
Citation Information:
[1]LIU Yashu,QIU Xiaohua,SUN Shimiao ,et al.Malware Detection Based on Two-dimensional Discrete Fourier Transform[J].Journal of Zhengzhou University(Natural Science Edition),2025,57(02):8-15.DOI:10.13705/j.issn.1671-6841.2023161.
Fund Information:
国家自然科学基金项目(62232016); 国家重点研发计划重点专项(2022YFC3800502)
2023-06-29
2023
2024-06-07
2025-03-18
2025
2
2024-03-14
2024-03-14
2024-03-14