光纤网络异常数据特征分类研究
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摘要
对光纤网络异常数据特征进行分类,可有效确定光纤网络出现异常的原因,提高光纤网络安全,降低其受到网络入侵的风险。目前对光纤网络中异常数据特征分类的方法未考虑异常数据的分布特点及复杂性,易陷入局部最优解,使得异常数据特征分类效果较差,不利于异常数据检测。提出基于支持向量机的光纤网络中异常数据特征分类研究。选取数据不同异常特征,以异常数据特征选择概率作为决策变量,利用粒子群方法对决策变量进行优化求解,产生微粒的引导,确定数据集分布性,跳出局部的最优,利用重采样、数据特征空间等一些相关的理论,经过数据分层抽样,利用支持向量机分类分布性光纤网络中数据异常特征。实验的结果表明,利用所提方法分类数据异常特征,能有效提高数据异常特征分类的效率与性能,为后续数据异常检测提供依据。
Classifying the abnormal data features of optical fiber network can effectively determine the reason of the fiber network anomaly,improve the security of optical fiber network and reduce the risk of network intrusion. At present,the method of abnormal data feature classification in optical fiber network does not consider the distribution and complexity of abnormal data,so it is easy to fall into the local optimal solution,which makes the classification effect of abnormal data feature worse,which is not conducive to abnormal data detection. In this paper,the feature classification of abnormal data in optical fiber network based on support vector machine(SVM) is proposed. Different abnormal features of data are selected,and the probability of feature selection of abnormal data is used as decision variable. Particle swarm optimization method is used to optimize the solution of decision variables. Generate particle guidance,determine the distribution of data sets,fix disturbed particles,jump out of local optimality,use resampling,data feature space and other relevant theories,through data stratified sampling,Support vector machine(SVM) is used to classify data anomalies in distributed fiber networks. The experimental results show that the proposed method can effectively improve the efficiency and performance of data anomaly feature classification and provide the basis for the subsequent data anomaly detection.
Classifying the abnormal data features of optical fiber network can effectively determine the reason of the fiber network anomaly,improve the security of optical fiber network and reduce the risk of network intrusion. At present,the method of abnormal data feature classification in optical fiber network does not consider the distribution and complexity of abnormal data,so it is easy to fall into the local optimal solution,which makes the classification effect of abnormal data feature worse,which is not conducive to abnormal data detection. In this paper,the feature classification of abnormal data in optical fiber network based on support vector machine(SVM) is proposed. Different abnormal features of data are selected,and the probability of feature selection of abnormal data is used as decision variable. Particle swarm optimization method is used to optimize the solution of decision variables. Generate particle guidance,determine the distribution of data sets,fix disturbed particles,jump out of local optimality,use resampling,data feature space and other relevant theories,through data stratified sampling,Support vector machine(SVM) is used to classify data anomalies in distributed fiber networks. The experimental results show that the proposed method can effectively improve the efficiency and performance of data anomaly feature classification and provide the basis for the subsequent data anomaly detection.
引文
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[2]刘志青,李鹏程,郭海涛,等.基于相关向量机的机械Li DAR点云数据分类[J].红外与激光工程,2016,45(s1):98-104.
[3]王末,王卷乐.Web环境下地学数据共享用户行为模式分析[J].地球信息科学学报,2016,18(9):1174-1183.
[4]陈栋,吴保国,陈天恩,等.分布式多源农林物联网感知数据共享平台研发[J].农业工程学报,2017,33(s1):300-307.
[5]赵健,陈宏,冼晓青,等.基于Web GIS的中国外来入侵物种地理分布信息系统的设计与开发[J].福建农业学报,2016,31(6):663-667.
[6]张家华,杨种学,王江平,等.融合DDo S威胁过滤与路由优化的SDN通信质量保障策略[J].电信科学,2015,31(4):121-127.
[7]向桢,向守兵.基于模糊遗传算法的数据库异常数据挖掘[J].控制工程,2017,24(5):947-951.
[8]刘志青,李鹏程,陈小卫,等.基于信息向量机的机载激光雷达点云数据分类[J].光学精密工程,2016,24(1):210-219.
[9]刘海涛,魏汝祥,袁昊劼.基于互信息的混合属性数据特征选择方法[J].海军工程大学学报,2016,28(4):78-84.
[10]冀素琴,石洪波,吕亚丽,等.基于粒化-融合的海量高维数据特征选择算法[J].模式识别与人工智能,2016,29(7):590-597.
[11]杨峻山,周家锐,朱泽轩,等.带约束小生境二进制粒子群优化的生物组学数据集成特征选择[J].信号处理,2016,32(7):757-763.
[12]苗鹏勇,王宝军,施斌,等.分布式光纤桩基检测数据智能化处理方案的研究与应用[J].工程地质学报,2017(6):1610-1616.
[13]赵理君,唐娉.典型遥感数据分类方法的适用性分析---以遥感图像场景分类为例[J].遥感学报,2016,20(2):157-171.
[14]王芳,朱晗,李云鹏,等.错位光纤干涉激光谱结合BP神经网络的温度传感研究[J].光谱学与光谱分析,2016,36(11):3732-3736.
[15]阎福礼,徐建国,鲁志弘.BJ-1智能小卫星多曝光量数据特征及其积雪提取方法研究[J].国土资源遥感,2016,28(1):28-34.
[16]陶偲,姚飞,刘守印.基于相位差测距的WSN节点测距数据滤波和定位算法的研究[J].电子设计工程,2017,25(7):35-40.