A User Behavior Anomaly Detection Approach Based on Sequence Mining over Data Streams

Abstract

How to design a low-latency and accurate approach for user behavior anomaly detection over data streams has become a great challenge. However, existing studies cannot meet low-latency and accurate requirements, due to a large number of subsequences and sequential relationship in behaviors. This paper presents BADSM, a user behavior anomaly detection approach based on sequence mining over data streams that seeks to address such challenge. BADSM uses self-adaptive behavior pruning algorithm to adaptively divide data stream into behaviors and decrease the number of subsequences to improve the efficiency of sequence mining. Meanwhile, the top-k abnormal scoring algorithm is used to reduce the complexity of traversal and obtain quantitative detection result to improve accuracy. We design and implement a streaming anomaly detection system based on BADSM to perform online detection. Extensive experiments confirm that BADSM significantly reduces processing delay by at least 36.8% and false positive rate by 6.4% compared with the classic sequence mining approach PrefixSpan.