Research on Online Monitoring and Analyzing of Interactive Behavior of Distributed Software

Abstract

The paper pays close attention to scenario and relationships between behavior and behavioral effects of distributed software at running time, presents a novel online monitoring and analyzing method for software behavior. Dynamic AOP monitoring technology is adopted to monitor interactive events related with business logic which are produced by the third party entities; Scenario-sensitive method is used to model complicated Interactive Behaviors (IBs) among these entities. By fusing real-time self-experience and pervious experience based on knowledge, the creditability of interactive entities is computed automatically. Multi-Entity Bayesian Network (MEBN) tool is adopted to construct reusable domain “knowledge fragment”. If current scenario is similar to pervious one, then pervious one is reused; if there is no similar scenario, evidences gained from monitoring and pervious experience are fused to construct behavior model for this scenario. The combination of large and small knowledge reuse improves analysis efficiency of IBs. Above method is used to “Trusted Purchasing Network” that we develop, deceitful or fraudulent behaviors in trade process are online monitored and analyzed.