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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >An algorithm for intelligent detection of network abnormal data in dynamic data environment
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An algorithm for intelligent detection of network abnormal data in dynamic data environment

机译：动态数据环境中网络异常数据智能检测算法

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摘要

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.
机译：目前，网络异常数据检测算法具有低效率和准确性，假负速率非常高。因此，异常数据的位置准确性并不理想。提出了一种基于时空最近邻和似然比测试的网络异常数据智能检测方法。基于改变平滑度判断策略的时间间隔调整算法和自适应数据改变规则用于根据网络性能参数自适应地调整数据采集时间间隔，实现网络数据获取。网格分区用于将源数据点转换为适当的粒度以完成数据预处理。基于最大后验概率，我们选择了在几个时刻检测到的数据的测量值，因为时间最近的邻点。量化的异常程度。同时，使用似然比测试来确定数据是否异常。异常警报信息被聚合。所有报警信息都根据尺寸排列。使用最大差值的两个闹钟时间用作边界，并且使用多点位错合并的异常位置方法来定位检测结果。实验结果表明，所提出的算法的平均检测时间为0.21秒。平均假负率为2.8％。数据检测的准确性和定位精度高。该算法可以有效地检测网络异常数据，这为此字段的开发奠定了基础。

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