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

Di Wu, Xiao Chen, Chao Chen, Jun Zhang, Yang Xiang, Wanlei Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

10 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationNetwork and System Security
Subtitle of host publication8th International Conference, NSS 2014 Xi’an, China, October 15-17, 2014 Proceedings
EditorsMan Ho Au, Barbara Carminati, C.-C. Jay Kuo
Place of PublicationCham Switzerland
Number of pages14
ISBN (Electronic)9783319116983
ISBN (Print)9783319116976
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Network and System Security 2014 - Xian China, Xi’an, China
Duration: 15 Oct 201417 Oct 2014
Conference number: 8th
https://link.springer.com/book/10.1007/978-3-319-11698-3 (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Network and System Security 2014
Abbreviated titleNSS 2014
Internet address

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