User abnormal behavior detection based on graph matching method

Currently, power grid enterprises have accumulated a large amount of user behavior data in their operations. Effective monitoring and analysis of user operation behavior in complex systems is crucial to ensure the safety and high quality operation of power grids. To address this need, this paper proposes a user abnormal behavior detection technique based on graph matching method. The technique transforms complex user behaviors into graph structures by constructing business process operation graphs and user temporal behavior links, and further converts them into analyzable numerical vectors. Based on these vectors, a Support Vector Machine (SVM) learning model is used and trained to achieve automatic user abnormal behavior detection. Finally, in-depth analyses and weighted calculations are performed to optimize the detection results of business abnormal behaviors of different systems to improve the accuracy and efficiency of detection. The results show that the method can effectively identify and analyze the abnormal behaviors of grid users, which is of great significance for enhancing the security and stability of the grid system.