Online support vector machine (SVM) is an effective learning method in real-time network traffic classification tasks. However, due to its geometric characteristics, the traditional online SVMs are sensitive to noise and class imbalance. In this paper, a scalable kernel convex hull online SVM called SKCHO-SVM is proposed to solve this problem. SKCHO-SVM involves two stages: (1) offline leaning stage, in which the noise points are deleted and initial pin-SVM classifier is built; (2) online updating stage, in which the classifier is updated with newly arrived data points, while carrying out the classification task. The noise deleting strategy and pinball loss function ensure SKCHO-SVM insensitive to noise data flows. Based on the scalable kernel convex hull, a small amount of convex hull vertices are dynamically selected as the training data points in each class, and the obtained scalable kernel convex hull can relieve class imbalance. Theoretical analysis and numerical experiments show that SKCHO-SVM has the distinctive ability of training time and classification performance.

中文翻译：

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用于智能网络流量分类的可扩展核凸壳在线支持向量机

在线支持向量机（SVM）是实时网络流量分类任务中的一种有效学习方法。但是，由于其几何特性，传统的在线SVM对噪声和类不平衡很敏感。为了解决这个问题，本文提出了一种可扩展的内核凸包在线支持向量机SKCHO-SVM。SKCHO-SVM包括两个阶段：（1）离线学习阶段，其中删除噪声点并建立初始的pin-SVM分类器；（2）在线更新阶段，其中在执行分类任务的同时用新到达的数据点更新分类器。噪声消除策略和弹球丢失功能确保SKCHO-SVM对噪声数据流不敏感。基于可扩展的内核凸包，动态地选择少量的凸壳顶点作为每个类中的训练数据点，所获得的可扩展核凸壳可以缓解类不平衡。理论分析和数值实验表明，SKCHO-SVM具有独特的训练时间和分类性能。