机器学习分布式网络传输异常数据智能检测方法

对于链路状态数据库的网络传输异常数据检测存在检测数据不完整、较为敏感、检测效率差的问题,提出基于机器学习的分布式网络传输异常数据智能检测方法,通过K最近邻分簇算法对分布式网络节点实施分簇,利用贝叶斯分类算法检测簇头是否出现异常;确定异常簇后,选取小波阈值降噪方法对异常簇内数据进行降噪处理,在此基础上,采用遗传算法检测降噪处理后异常簇内的异常数据,通过群体内最佳个体与最差个体的适应度函数值的差值同既定阈值的比较结果得到最终异常数据。经实验证明,所提方法检测异常数据的平均时间为8.48 s,检测结果与实际结果相似性较高,且检测性能较为稳定,说明该方法具有较高的异常数据检测性能。

Intelligent detection method for distributed network transmission abnormal data based on machine learning

At present,there are some problems in the detection of abnormal network transmission data in link state database,such as incomplete,sensitive and poor detection efficiency.Based on this,an intelligent detection method of abnormal data in distributed network transmission based on machine learning is proposed.The nodes in the distributed network are clustered by K-nearest neighbor clustering algorithm,and whether the cluster head is abnormal is detected by Bayesian classification algorithm.After determining the abnormal cluster,the wavelet threshold denoising method is selected to denoise the abnormal data in the cluster.On this basis,genetic algorithm is used to detect the abnormal data after noise reduction,the abnormal data in the abnormal cluster is measured,and the final abnormal data is obtained by comparing the difference between the fitness function value of the best individual and the worst individual in the group with the given threshold value.The experimental results show that the average time of detecting abnormal data is about 8.48 s,the detection results are similar to the actual results,and the detection performance is relatively stable,which shows that the method has high performance of abnormal data detection.