A Kind of Malicious Code Detection Scheme Based on Fuzzy Reasoning

Author

Guo Gang ; Chen Zhongquan

Author_Institution

Sch. of Comput. Sci., Beijing Inst. of Technol., Beijing, China

fYear

2014

fDate

25-26 Oct. 2014

Firstpage

19

Lastpage

22

Abstract

This thesis presents a malicious-degree decision system based on dynamic fuzzy neural network. Integrated with fuzzy reasoning and neural network in artificial intelligence, this system gives a comprehensive evaluation such as malicious-degree by analyzing the behaviors of unknown code. This method is compared with simple and multiple Bayes in the end. The experimental results and comparison show that this decision system can achieve good results in detecting polymorphic and unknown viruses.

Keywords

computer viruses; fuzzy neural nets; fuzzy reasoning; fuzzy set theory; Bayes method; artificial intelligence; comprehensive evaluation; dynamic fuzzy neural network; fuzzy reasoning; malicious code detection scheme; malicious-degree decision system; polymorphic unknown virus detection; unknown code behavior analysis; Accuracy; Artificial neural networks; Bayes methods; Fuzzy neural networks; Fuzzy reasoning; Mathematical model; artificial neural network; fuzzy reasoning; malicious code detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2014 7th International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4799-6635-6

Type

conf

DOI

10.1109/ICICTA.2014.12

Filename

7003475