物联网通信异常数据的检测方法研究
发布时间：2019-02-13 03:07
【摘要】：在物联网多设备通信过程中,其差异化的数据在分类种类上存在较大的问题,导致识别过程存在异常数据不收敛或者无穷计算的问题。为了解决这一问题,提出基于多约束标签的异常数据检测方法,在对海量的异常数据进行分类的过程中,引入可以约束标签异常特征的多个约束条件,对物联网通信数据进行降维限制处理,避免扩大化的搜索过程,再运用支持向量机在限制区域内完成检测分类。实验结果表明,利用该算法能对海量的物联网通信异常数据进行自动学习过程的搜索,提高异常数据检测的准确性。
[Abstract]:In the process of multidevice communication in the Internet of things, there is a big problem in the classification of the differentiated data, which leads to the problem of abnormal data convergence or infinite computation in the identification process. In order to solve this problem, a method of anomaly data detection based on multi-constraint tags is proposed. In the process of classifying large amounts of abnormal data, several constraints that can constrain the abnormal features of labels are introduced. In order to avoid the expansion of the search process, support vector machine (SVM) is used to complete the detection and classification in the restricted area by reducing the dimension of the communication data of the Internet of things. The experimental results show that the algorithm can automatically search the massive abnormal data of Internet of things communication and improve the accuracy of anomaly data detection.
【作者单位】： 宿迁学院信息工程学院;江苏大学计算机科学与通信工程学院;
【基金】：宿迁市科技计划项目(Z201445,S201410,Z201448) 宿迁学院科研基金项目(2013KY13)
【分类号】：TP391.44;TN929.5
本文编号：2421106
[Abstract]:In the process of multidevice communication in the Internet of things, there is a big problem in the classification of the differentiated data, which leads to the problem of abnormal data convergence or infinite computation in the identification process. In order to solve this problem, a method of anomaly data detection based on multi-constraint tags is proposed. In the process of classifying large amounts of abnormal data, several constraints that can constrain the abnormal features of labels are introduced. In order to avoid the expansion of the search process, support vector machine (SVM) is used to complete the detection and classification in the restricted area by reducing the dimension of the communication data of the Internet of things. The experimental results show that the algorithm can automatically search the massive abnormal data of Internet of things communication and improve the accuracy of anomaly data detection.
【作者单位】： 宿迁学院信息工程学院;江苏大学计算机科学与通信工程学院;
【基金】：宿迁市科技计划项目(Z201445,S201410,Z201448) 宿迁学院科研基金项目(2013KY13)
【分类号】：TP391.44;TN929.5
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，本文编号：2421106
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