Research of user abnormal behavior recognition model for water economy operating system

Abstract

The water economic operation system is an important tool for water resources management and can greatly improve the operating efficiency of power stations. However, user behavior directly affects the system's operating status and data accuracy. Through real-time monitoring and analysis of user behavior via system logs, potential security threats can be discovered in a timely manner. The commonalities of abnormal user behaviors found in system logs were studied. A Transformer-based user abnormal behavior identification model for water economic operation system(T-UABI-WEOS) was proposed. Without sacrificing the original information of the data, the feature fusion preprocessing method is used to optimize the expression form of the data, so that the interaction between features is effectively reflected, rather than simply being treated independently. Considering the imbalance of user behavior data, a variational auto-encoder(VAE) model to learn from normal sequence data was introduced. The trained VAE model then generates simulated abnormal sequence data to balance the dataset, thus enhancing the training effect of the model. Experimental result show that the proposed method achieves a 6% improvement in prediction accuracy over traditional data preprocessing method. Additionally, the experiment compared different deep learning models, and the model T-UABI-WEOS showed higher accuracy and lower false alarm rate. The result demonstrated that T-UABI-WEOS achieved higher accuracy and a lower false alarm rate. The research result provide scientific decision-making support for the electric power industry. By identifying abnormal user behavior in real-time, potential security threats can be discovered promptly, allowing for the implementation of corresponding preventative measures. The approach can better address network security incidents and ensure the stable operation of the power grid, ultimately contributing to national security and stability.

Keywords

user behavior log / feature fusion / anomaly detection / water economy operating system