Journal of South China University of Technology (Natural Science Edition) ›› 2009, Vol. 37 ›› Issue (5): 94-100.

• Computer Science & Technology • Previous Articles     Next Articles

Behaviour Trust Control Based on Bayesian Networks and User Behavior Log Mining

Zhao Jie  Xiao Nan-feng  Zhong Jun-rui   

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, Guangdong, China
  • Received:2008-06-26 Revised:2008-11-29 Online:2009-05-25 Published:2009-05-25
  • Contact: 赵洁（1979-），女，在职博士生，广东工业大学讲师，主要从事智能计算、电子商务研究． E-mail:kitten．zj@163．com
  • About author:赵洁（1979-），女，在职博士生，广东工业大学讲师，主要从事智能计算、电子商务研究．
  • Supported by:

    国家自然科学基金委员会与中国民用航空总局联合资助项目（60776816）;广东省自然科学基金重点项目（8251064101000005）;广东省科技计划项目（20078060401007）;广东工业大学青年基金资助项目（072058）

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Abstract:

The existing evaluation methods of network user behaviors are of high cost and low practicability. In or- der to effectively forecast and evaluate network user behavior trust with ease, a trust forecast and control algorithm of user behaviors is proposed based on Bayesian networks, in which the clustering algorithm and the distribution density function are used to set parameters, and the corresponding relationship between quantitive evidence and trust grade is obtained. Afterwards, the configurable plug-in of trust management is implemented based on IIS and. Net framework to create user behavior logs, thus providing evidences for the forecast and control algorithm and avoiding the data cleaning of common Web logs. Experimental results indicate that the proposed algorithm is capable of predicting the trust grade in multi-trust-attribute conditions, improving the security and reliability of the server and restricting the trade behaviors of the user.

Key words: trust management, behavior trust, Bayesian networks, user behavior, log mining