计算机科学
初始化
多类分类
概念漂移
人工智能
机器学习
滑动窗口协议
支持向量机
变量(数学)
数据挖掘
数据流挖掘
窗口(计算)
数学分析
数学
程序设计语言
操作系统
作者
Weike Liu,Cheng Zhu,Zhaoyun Ding,Hang Zhang,Qingbao Liu
标识
DOI:10.1016/j.engappai.2022.105607
摘要
The complex problems of multiclass imbalance, virtual or real concept drift, concept evolution, high-speed traffic streams and limited label cost budgets pose severe challenges in network traffic classification tasks. In this paper, we propose a multiclass imbalanced and concept drift network traffic classification framework based on online active learning (MicFoal), which includes a configurable supervised learner for the initialization of a network traffic classification model, an active learning method with a hybrid label request strategy, a label sliding window group, a sample training weight formula and an adaptive adjustment mechanism for the label cost budget based on a periodic performance evaluation. In addition, a novel uncertain label request strategy based on a variable least confidence threshold vector is designed to address the problems of a variable multiclass imbalance ratio or even the number of classes changing over time. Experiments performed based on eight well-known real-world network traffic datasets demonstrate that MicFoal is more effective and efficient than several state-of-the-art learning algorithms.
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