Analyzing Running-time Behavioral Credibility for Distributed Software Based on Bayesian Network
In an open,dynamic and complex internet environment,monitoring and analyzing behavioral credibility is essential for the modern distributed software.It is a good idea to build a Bayesian network with specific interaction scenarios through collecting relevant data systematically,which can analyze software behavior′s credibility in a specific context for the running-time performance with some external features of distributed software.In network-building process,this paper proposes a so-called "3σ principle" to make the continuous random variable be discrete,which has two unique advantages in marking sample and determining the priori parameters of Bayesian learning.The "3σ principle" accords to actual situation of running-time software and operates easily,which can improve efficiency in the parameter learning.At the same time,this paper proposes a method to construct a Bayesian network hierarchically by calculating the correlation coefficient between two nodes in order to modify the network structure,which can reduce the complexity and shrink the error during building network.The experiments illuminate that the method proposed in this paper has unique advantage in credible analysis of software behavior.