Deep learning-based malicious code sample classification method and system

Abstract

The invention discloses a deep learning-based malicious code sample classification method. The method comprises the following steps: disassembling malicious code samples to obtain assembly codes; converting the assembly codes into image sample data on the basis of a corresponding relationship between a hexadecimal code and an image gray value; and inputting the image sample data into a pre-trained convolutional neural network so as to a malicious code sample classification result. The invention furthermore discloses a deep learning-based malicious code sample classification system. According to the deep learning-based malicious code sample classification method and system, classes of the malicious code samples can be correctly recognized, and the correctness of the malicious code sample classification is improved.

Description

technical field [0001] The present invention relates to the technical field of information security, in particular to a method and system for classifying malicious code samples based on deep learning. Background technique [0002] Malicious code (Unwanted Code) refers to code that has no effect but brings danger, also known as malware. Ed Skoudis defines malware as a set of instructions that run on a computer to make the system perform tasks according to the attacker's wishes; Microsoft uses malware to refer to worms, viruses, and Trojan horses that deliberately perform malicious tasks in a computer system. Malicious code classification is an important content in the field of malicious code research. Scholars at home and abroad have conducted in-depth research on this. Existing automatic malicious code analysis technologies mainly include static analysis and dynamic analysis: (1) Static analysis technology is based on File structure analysis, disassembly, decompilation and ...

AI Technical Summary

Problems solved by technology

The technical scheme described in the present invention can overcome the problems of low efficiency and low accuracy of traditional classification methods

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