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

Paper


Wu, Di, Chen, Xiao, Chen, Chao, Zhang, Jun, Xiang, Yang and Zhou, Wanlei. 2015. "On addressing the imbalance problem: a correlated KNN approach for network traffic classification." NSS 2014: 8th International Conference on Network and System Security. Xi'an, China 15 - 17 Oct 2014 Switzerland . Springer. https://doi.org/10.1007/978-3-319-11698-3_11
Paper/Presentation Title

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

Presentation TypePaper
AuthorsWu, Di, Chen, Xiao, Chen, Chao, Zhang, Jun, Xiang, Yang and Zhou, Wanlei
Journal or Proceedings TitleProceedings of the 8th International Conference on Network and System Security (NSS 2014)
Journal Citation8792, pp. 138-151
Number of Pages14
Year2015
PublisherSpringer
Place of PublicationSwitzerland
ISBN9783319116976
9783319116983
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-319-11698-3_11
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-319-11698-3_11
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-3-319-11698-3
Conference/EventNSS 2014: 8th International Conference on Network and System Security
Event Details
NSS 2014: 8th International Conference on Network and System Security
Parent
International Conference on network and System Security
Delivery
In person
Event Date
15 to end of 17 Oct 2014
Event Location
Xi'an, China
Abstract

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.

ANZSRC Field of Research 20204604. Cybersecurity and privacy
Public Notes

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SeriesLecture Notes in Computer Science
Byline AffiliationsDeakin University
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