Malicious software classification method based on static analysis

Abstract

The invention belongs to the technical field of computer security, and particularly relates to a malicious software classification method based on static analysis. The malicious software classification method comprises the following steps: converting malicious software into a binary file, generating a grayscale image, training the grayscale image by adopting a convolutional neural network model with a spatial pyramid pooling layer so as to obtain a static classifier, and carrying out family classification on malicious software samples through the static classifier. According to the malicious software classification method, the grayscale image can be used as a feature to classify malicious software, so that information loss caused in an image preprocessing stage is effectively reduced. According to the malicious software classification method, the malicious software is classified by analyzing the contour features of the malicious software, so that professionals can be helped to reduce the cost of identifying the malicious software.

Description

technical field [0001] The invention belongs to the technical field of computer security, and in particular relates to a method for classifying malicious software based on static analysis. Background technique [0002] With the rapid development of the Internet industry, people's dependence on various software has also increased, which has brought great convenience to the attack and spread of malware. Due to the proliferation of automated tools, malware is discovered at a much slower rate than it can be found on the Internet. For example, in 2017 Kaspersky Labs detected 15,714,700 malicious objects. McAfee Labs detected 7.9 million malicious files per day in Q1 2018, an increase of 4.5 million from Q4 2017. Although the derivation speed of malware is getting faster and faster, the vast majority of malware evolves through polymorphism and deformation of known malware. Therefore, discovering the homology relationship in the samples is very important for tracing the source o...

AI Technical Summary

Problems solved by technology

Although relevant personnel have done a lot of research work, malware is still rampant

The accuracy of the dynamic analysis method is high but the efficiency is poor, and excessive classification costs will be generated during the analysis process

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