基于数据挖掘的光纤通信网络异常数据检测研究

Research on abnormal data detection of optical fiber communication network based on data mining

为了提高光纤通信网络中异常数据的识别能力,提出了基于熵目标函数最优化的异常数据检测算法。首先,对数据样本进行属性分类,依据异常数据特征密度指标完成邻域区间半径的选取;其次,通过对高阶统计量的大数据聚类度循环迭代,完成特征提取参数的优化;最后,由样本属性概率计算熵目标函数的最优值,并利用最优值完成异常数据检测。实验对1 000组通信数据进行测试,结果显示,该算法的检测精度均值约为95.7%,其数据融合率、检测耗时与平均误检率均优于2种传统方法。该算法具有精度高、收敛快、误检率低的优势,具有一定的应用价值。

In order to improve the recognition ability of abnormal data in optical fiber communication networks,an abnormal data detection algorithm based on the optimization of entropy objective function was proposed.Firstly,the attributes of the data samples were classified,and the radius of the neighborhood interval was selected based on the abnormal data feature density index.Secondly,the clustering degree of the big data with high-order statistics was iterated and the optimization of the feature extraction parameters was completed.Finally,the optimal value of the entropy objective function was calculated according to the sample attribute probability,and it was used to complete the abnormal data detection.The experiment tested 1000 sets of communication data.The results show that the average detection accuracy of this algorithm is about 95.7%,and its data fusion rate,detection time-consuming and average false detection rate are better than that of the two traditional methods.It can be seen that this algorithm has the advantages of high accuracy,fast convergence and low false detection rate,which has the certain application value.