Analysis of Network Data Flow Anomaly Detection Based on Density Clustering
This paper describes a density clustering based network data flow anomaly detection algorithm,which not only adapts to changes in the distribution of data samples,but also innovatively proposes a dual clustering class graph algorithm to avoid deleting potential micro clusters as true outliers during the anomaly detection stage.This technology can significantly improve detection performance,and its speed and effectiveness have been demonstrated through experiments on multiple datasets.
intelligent controlnetwork data flowanomaly detectiondensity clusteringstreaming data
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