Traffic Classification Using an Efficient Lightweight Convolutional Network
Pages 167 - 172
Abstract
Traffic classification is playing a key role in network security domain with rapid growth of current Internet network, since traffic characterization is an important step for network management and network anomaly detection. Numerous researches have been done on this topic which have led to many different methods. Most of them use predefined features extracted by an expert to classify network traffic, which is costly and time consuming. In contrast, in the work, we propose a deep learning (DL) based approach, and it can automatically extract and select features through training, which has made DL-based method a highly desirable approach for traffic classification. Especially, inspired by the ConvNeXt, we believe that, compared with other DL-based method, 2D ConvNet can achieve a better performance by training techniques, while maintaining the simplicity and efficiency of standard ConvNets. Experimental results have verified this. After an initial pre-processing phase on data, the data are fed into DL framework to classify network traffic. Experiments have demonstrated that the proposed method enhanced the initial DL architecture (99.24% accuracy), and achieved accuracy of 99.47% in traffic categorization on UNB ISCX VPN-nonVPN dataset.
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Published In
December 2022
365 pages
ISBN:9781450398039
DOI:10.1145/3579895
Copyright © 2022 ACM.
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Association for Computing Machinery
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Publication History
Published: 04 April 2023
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ICNCC 2022
ICNCC 2022: 2022 The 11th International Conference on Networks, Communication and Computing
December 9 - 11, 2022
Beijing, China
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