Volume 5 Number 4 (Apr. 2010)
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JCP 2010 Vol.5(4): 581-588 ISSN: 1796-203X
doi: 10.4304/jcp.5.4.581-588

A Hybrid Intelligent Learning Algorithm to Identify the ECNS Based on FBP Optimized by GA

Zhibin Liu1 and Shuanghai Li2
1 Economics and Management Department, North China Electric Power University, Baoding City, China
2 College of Business Administration, Sichuan University, Chengdu City, China


Abstract—Along with the development of computer network, the electronic commerce has become the new pattern to carry on the commercial activity gradually, but the security problem is also getting more and more prominent. How to identify the E-commerce network security (ECNS) rating and establish a security convenient application environment for the electronic commerce has already become a major concern topic that needs to be settled urgently. To identify the ECNS rating scientifically and accurately, this paper proposes a hybrid intelligent learning algorithm which uses the genetic algorithm (GA) to optimize the fuzzy backpropagation (FBP) neural network. The algorithm not only can exert the unique advantages of BP neural network (BPNN), but also overcome the shortcoming to produce the local minimum points in the network modeling process and enhance the accuracy of network security identification greatly. The ECNS identification results for 14 E-commerce systems show that the method is reliable and efficiency.

Index Terms—hybrid intelligent algorithm, FBP, GA, ECNS, security rating identification

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Cite: Zhibin Liu and Shuanghai Li, " A Hybrid Intelligent Learning Algorithm to Identify the ECNS Based on FBP Optimized by GA," Journal of Computers vol. 5, no. 4, pp. 581-588, 2010.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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