Abstract: With the continuous advancement of informatization,networking,and intelligence in the power system,the power monitoring system is facing increasingly severe network security threats.It is particularly important to conduct a comprehensive and in-depth analysis of the multi-source data covered by the power monitoring system network,taking into account various factors such as network asset security risks,user behavior,and business characteristics.Based on this,a multi-source data cleaning me-thod and intrusion detection method for power monitoring systems are proposed.The improved maximum correlation and minimum redundancy algorithm is used to select the multi-source security data features of the power monitoring system network,retain appropriate security data features,and use a network intrusion detection model to detect and classify multi-source network security data,effectively solving the problem of complex feature attributes of multi-source data in power monitoring systems leading to decreased accuracy of model classification in the later stage.Simulation experiments show that the proposed multi-source data feature selection method and intrusion detection algorithm have significantly improved the detection rate and classification accuracy of attacks on power monitoring systems.

Key words: Power monitoring system, Long short-term memroy, Feature processing, Intrusion detection, Data cleaning