Malware Detection Based on Two-dimensional Discrete Fourier Transform
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 frame-work,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 de-tect 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.
万方数据
维普
