Classification of malicious software behaviour detection with hybrid set based feed forward neural network

Authors:
Yong Wang;Dawu Gu;Mi Wen;Haming Li;Jianping Xu
Affiliations:
Department of Computers Science and Technolgy, Shanghai University of Electric Power, Shanghai, China;Department of Computers Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Department of Computers Science and Technolgy, Shanghai University of Electric Power, Shanghai, China;Department of Computers Science and Technolgy, Shanghai University of Electric Power, Shanghai, China;Department of Computers Science and Technolgy, Shanghai University of Electric Power, Shanghai, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

Behavior detection of malicious software is better than signature-based detection method when used to find unknown malicious software The paper presents a classification method of malicious software behavior detection with hybrid set based feed forward neural network We choose malicious software detection database for test with 57345 records from National Anti-Computer Intrusion and Anti-Virus Research Center According to the definition of selected data set relations and transfer functions, the weighted path length trees of malicious software detection data are calculated for neural network input vectors After repeat training, different malicious software detection methods can be classified by the method with the about 83.9 percent right classification.