采用原型学习的类概念漂移网络数据检测与分类算法  

Class Concept Drift Network Data Detection and Classification Algorithm Based on Prototype Learning

作　　者：陈坤 李青[1] 褚瑞娟 樊讯池 王润泽 CHEN Kun;LI Qing;CHU Ruijuan;FAN Xunchi;WANG Runze(Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学,河南郑州450001

出　　处：《信息工程大学学报》2025年第1期14-20,共7页Journal of Information Engineering University

摘　　要：受网络设备更新、通信协议升级等影响,网络数据的分布、类别和属性发生不可预知的漂移特性,导致基于机器学习的网络数据分类模型的分类精度下降。针对此问题,提出一种采用原型学习的类概念漂移网络数据检测与分类算法。该算法从时间序列的角度处理网络数据,利用带有注意力机制的网络提取数据的时空特征。借鉴原型学习思想,使用样本与原型之间的距离进行分类。当发生类概念漂移时,设定合适的阈值以区分新类,并使用其均值更新原型矩阵。实验结果表明,使用原型匹配分类不仅比传统的softmax分类器准确率高,且当数据发生类概念漂移时,所提算法能够有效检测漂移,并在漂移数据上能够表现出较好的分类性能。Affected by network equipment update and communication protocol upgrade,the distribution,category and attribute of network data have unpredictable drift characteristics,subsequently impairing the classification precision of machine learning-based network data classification models.To solve this problem,a class concept drift network data detection and classification algorithm based on prototype learning is proposed.The network data is addressed from the time series perspective,harnessing a network equipped with an attention mechanism to distill spatiotemporal features from the data.Drawing on the principles of prototype learning,the distances between samples and prototypes are utilized for classification purposes.In instances of class concept drift,a suitable threshold is established to identify novel classes,and the mean values are employed to refresh the prototype matrix.Experiment result shows that the utilization of prototype matching for classification not only yields higher accuracy than traditional softmax classifiers,but also can effectively detect the drift when the data has class concept drift,and has better classification performance on the drift data.

关 键 词：原型学习 概念漂移 新类检测 网络数据 

分 类 号：TN911.6[电子电信—通信与信息系统]