Malware visualization and automatic classification with enhanced information density

doi:10.16511/j.cnki.qhdxxb.2018.22.054

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Abstract The development of computers and networking has been accompanied by exponential increases in the amount of malware which greatly threaten cyber space applications. This study combines the reverse analysis of malicious codes with a visualization method in a method that visualizes operating code sequences extracted from the ".text" section of portable and excutable (PE) files. This method not only improves the efficiency of malware, but also solves the difficulty of simHash similarity measurements. Tests show that this method identifies more effective features with higher information densities. This method is more efficient and has better classification accuracy than traditional malware visualization methods.

Keywords malware visualization simHash image texture

Issue Date: 16 January 2019

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	LIU Yashu
	WANG Zhihai
	HOU Yueran
	YAN Hanbing

Cite this article:

LIU Yashu,WANG Zhihai,HOU Yueran, et al. Malware visualization and automatic classification with enhanced information density[J]. Journal of Tsinghua University(Science and Technology), 2019, 59(1): 9-14.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2018.22.054 OR http://jst.tsinghuajournals.com/EN/Y2019/V59/I1/9

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