An abnormal behavior detection algorithm based on fuzzy clustering for multi-categories affiliation of power entities

Abstract

Aiming at the problems of complex behavior of power entities and concealed attack means in the digital active power grid,a multi-category attribution anomaly detection algorithm based on fuzzy clustering was proposed.Firstly,the similarity measurement method of power entity behavior was optimized,a fuzzy clustering algorithm was constructed based on the measurement value,and the membership matrix of entity behavior corresponding to various classes was obtained through several iterations.Secondly,the nearest neighbor distance,nearest neighbor density,and nearest neighbor relative anomaly factor of entities in each category were calculated according to the category softening membership matrix.Finally,the relative abnormal situation of the entity in various clusters was analyzed to judge whether the behavior of the power entity belongs to the abnormal behavior category.The results show that compared with LocalOutlier Factor(LOF),K-means,and RandomForest algorithms,the new method has detected more abnormal behaviors and achieved better anomaly detection evaluation indexes.The problem of a single evaluation angle of samples in traditional anomaly detection algorithms was solved and the ability of the digital active power grid to resist unknown threats was improved.

Keywords

• data security and computer security; user and entity behavior analysis; digital active power grid; fuzzy clustering; anomaly detection