An Approach Of Anomaly Diagnosis With Logs For Distributed Services In Communication Network Information System

Anomaly diagnosis for distributed service plays an important role in communication network information system. Log analysis is the main method to undertake anomaly detection. In order to reduce the manual detection, we propose an anomaly detection method based on the time-weighted control flaw graph model. The border is split by a discrete degree strategy based on analyzing the time interval distribution and the time weight is selected to be k-means. Experiments show that our algorithm has good precision and recall in anomaly diagnosis. In real-world scenarios, it has a precision of 80% and a recall rate of 65% on average.