User abnormal behavior detection based on graph matching method

Yongqiang Wang; Hong Hu; Yingcong Chen; Zhiming Chen; Zilong Li

doi:10.1117/12.3042484

10 September 2024 User abnormal behavior detection based on graph matching method

Yongqiang Wang, Hong Hu, Yingcong Chen, Zhiming Chen, Zilong Li

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 132570R (2024) https://doi.org/10.1117/12.3042484
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongqiang Wang, Hong Hu, Yingcong Chen, Zhiming Chen, and Zilong Li "User abnormal behavior detection based on graph matching method", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 132570R (10 September 2024); https://doi.org/10.1117/12.3042484

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