PADM: Page Rank-Based Anomaly Detection Method of Log Sequences by Graph Computing

With the popularity of various software applications in cloud computing, software exception becomes an important issue. How to detect the exceptions more quickly seems to be crucial for the software service company. To solve the above problem, this paper presents an efficient log anomaly detection method named PADM (Page Rank-based Anomaly Detection Method) based on the graph computing algorithm. In this method, the logs are transformed into a graph to represent the complex relationship between the log records, then we design an extended Page Rank algorithm based on the graph to get the importance score for each log. After that, we compare the scores to that of the training logs to determine whether they are abnormal or not. Finally, we compare PADM with other anomaly detection methods on the real logs, and the results show that it outperforms the currently widely used mechanisms with higher accuracy, lower time complexity and better scalability.