Abnormal Flow Monitoring Method of Power Grid Equipment Based on Isolation Forest Technology

With the rapid development of smart grid, the network security of power grid equipment is facing increasingly severe challenges, among which abnormal flow monitoring is a key link in ensuring network security. This paper proposes an abnormal flow monitoring method for network security of power grid equipment, aiming to improve monitoring efficiency and accuracy. The adaptive neighborhood algorithm is used to moderately reduce the dimension of power equipment flow data to reduce data redundancy and retain key features. The parallel decomposition sensing algorithm is used to realize the parallel decomposition of multi-channel data flow, which effectively reduces the mutual interference between data. The decomposed data is input into the detection model based on the isolation forest model, and the abnormal flow is identified by calculating the abnormal score of the sample point. The experimental results show that the method shows superior performance in terms of mean square error, time consumption and recall rate, with small error, high detection efficiency and strong anomaly recognition ability, which provides reliable technical support for the network security of power grid equipment and has high practical value.