Abnormal Detection of Electric Management System based on Spatial-temporal User Profile

Abnormal detection of user behavior is essential for Electric Management Information System to ensure its stable operations. In this paper, we propose a user profile based abnormal behavior detection algorithm based on spatial-temporal data mining. First, the user log recording the operation and corresponding timestamps are generated and aggregated according to the user catalog. Then the semantic tags to describe the users are defined and detected from the user log files. Next user profile is generated based on the detected semantic tags. Finally, we detect the abnormal behavior by comparing the user profile with the profile of the group that user belongs to. The algorithm proposed in this paper can detect the regular abnormal behavior such as working late or working overtime, which is proofed by experiment results.