On addressing the imbalance problem: a correlated KNN approach for network traffic classification

Wu, Di, Chen, Xiao, Chen, Chao, Zhang, Jun, Xiang, Yang, and Zhou, Wanlei (2014) On addressing the imbalance problem: a correlated KNN approach for network traffic classification. In: Lecture Notes in Computer Science (8792) pp. 138-151. From: NSS 2014: 8th International Conference on Network and System Security, 15-17 October 2014, Xi'an, China.

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With the arrival of big data era, the Internet traffic is growing exponentially. A wide variety of applications arise on the Internet and traffic classification is introduced to help people manage the massive applications on the Internet for security monitoring and quality of service purposes. A large number of Machine Learning (ML) algorithms are introduced to deal with traffic classification. A significant challenge to the classification performance comes from imbalanced distribution of data in traffic classification system. In this paper, we proposed an Optimised Distance-based Nearest Neighbor (ODNN), which has the capability of improving the classification performance of imbalanced traffic data. We analyzed the proposed ODNN approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments were implemented on the real-world traffic dataset. The results show that the performance of “small classes” can be improved significantly even only with small number of training data and the performance of "large classes" remains stable.

Item ID: 64415
Item Type: Conference Item (Research - E1)
ISBN: 978-3-319-11697-6
Copyright Information: © Springer International Publishing Switzerland 2014
Date Deposited: 30 Sep 2020 23:21
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0803 Computer Software > 080303 Computer System Security @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%
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