AIMS: A Predictive Web API Invocation Behavior Monitoring System

Lanxuan Tong, Jian Cao, Qing Qi, Shiyou Qian

Abstract: With the extensive applications of Web APIs, to monitor and analyze personal Web API invocation behaviors is becoming more and more important. However, due to the fact that the users' Web API invocation behaviors are highly heterogeneous, it is extremely challenging to provide a unified framework. In the paper, we introduce a predictive Web API Invocation Behavior Monitoring System (AIMS). AIMS automatically analyzes the predictability of users' invocation behaviors and for the user whose behaviors can be predicted, an adaptive strategy is applied to generate the predictions in an adaptive way. In addition, a context-aware K-nearest neighbor classifier is applied to detect the anomaly of user's invocation behaviors. Experiments on both a real-world dataset and synthetic dataset show AIMS is efficient in analyzing personal API invocation behaviors.