关键词: 信任管理, 行为信任, 贝叶斯网络, 用户行为, 日志挖掘

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