An algorithm for intelligent detection of network abnormal data in dynamic data environment

Abstract

At present, network abnormal data detection algorithm has low efficiency and accuracy, and the false negative rate is very high. Therefore, the location accuracy of abnormal data is not ideal. An intelligent detection method of network abnormal data based on space-time nearest neighbor and likelihood ratio test was proposed. The time interval adjustment algorithm based on the change smoothness judgement strategy and the adaptive data change rule was used to adaptively adjust data acquisition time interval according to network performance parameters and achieve network data acquisition. The grid partition was used to convert source data points into appropriate granularity to complete the data preprocessing. Based on the maximum a posteriori probability, we selected the measured values of data to be detected at several moments as the time nearest neighbor points. The abnormal degree of data was quantified. Meanwhile, the likelihood ratio test was used to determine whether the data was abnormal. The abnormal alarm information was aggregated. All alarm information was arranged according to the size. The two alarm times with maximum difference value are used as the boundary, and the multi-point dislocation combined abnormal location method was used to locate the detection result. Experiment results show that the average detection time of proposed algorithm is 0.21 s. The average false negative rate is 2.8%. The accuracy of abnormal data detection and the positioning accuracy are high. The proposed algorithm can detect network abnormal data efficiently, which lays a foundation for the development of this field.