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Scalable kernel convex hull online support vector machine for intelligent network traffic classification

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Abstract

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.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grants 61976028 and 61806026 and by the Natural Science Foundation of Jiangsu Province under Grant BK 20180956.

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Authors and Affiliations

  1. School of Information Science and Engineering, Changzhou University, Changzhou, 213164, China

    Xiaoqing Gu & Tongguang Ni

  2. Viterbi School of Engineering, University of Southern California, Los Angeles, 90089, USA

    Yiqing Fan

  3. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Weibo Wang

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  1. Xiaoqing Gu
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  2. Tongguang Ni
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  4. Weibo Wang
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Correspondence to Tongguang Ni.

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Gu, X., Ni, T., Fan, Y. et al. Scalable kernel convex hull online support vector machine for intelligent network traffic classification. Ann. Telecommun. 75, 471–486 (2020). https://doi.org/10.1007/s12243-020-00767-2

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