Classification method for malicious code based on A_Kohonen neural network

Abstract

The invention discloses a classification method for a malicious code based on an A_Kohonen neural network, belonging to the technical field of computer network security. According to the invention, an artificial neural network is introduced into the classification of the malicious code for the first time, and the existing unsupervised learning Kohonen neural network is improved. After the unsupervised learning in the first stage, a supervised learning process is added, so that the classification precision is improved. By the method, the fast and precision classification for an unknown malicious code becomes possible, and the algorithm is simple and real-time.

Description

technical field [0001] The invention relates to a malicious code classification method, in particular to a malicious code classification method based on the A_Kohonen neural network improved by the Kohonen neural network, and belongs to the technical field of computer network security. Background technique [0002] Malicious codes are a group of programs that infect other software by replicating themselves, including traditional computer viruses, network worms, and Trojan horses. With the development of technology, the types and quantities of malicious codes are in an explosive development trend. There is a certain lag in the response of traditional anti-virus software systems to the continuous emergence of malicious codes. In order to make up for this defect and respond to various malicious codes appearing on the Internet as soon as possible, Rising, Trend Micro, Kaspersky, McAFee, Symantec, Jiangmin Technology, PANDA, Kingsoft, 360, etc. have launched their own cloud secu...

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