Zero-day traffic identification

Jun Zhang, Xiao Chen, Yang Xiang, Wanlei Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

Recent research on Internet traffic classification has achieved certain success in the application of machine learning techniques into flow statistics based method. However, existing methods fail to deal with zero-day traffic which are generated by previously unknown applications in a traffic classification system. To tackle this critical problem, we propose a novel traffic classification scheme which has the capability of identifying zero-day traffic as well as accurately classifying the traffic generated by pre-defined application classes. In addition, the proposed scheme provides a new mechanism to achieve fine-grained classification of zero-day traffic through manually labeling very few traffic flows. The preliminary empirical study on a big traffic data show that the proposed scheme can address the problem of zero-day traffic effectively. When zero-day traffic present, the classification performance of the proposed scheme is significantly better than three state-of-the-art methods, random forest classifier, classification with flow correlation, and semi-supervised traffic classification.
Original languageEnglish
Title of host publicationCyberspace Safety and Security
Subtitle of host publication5th International Symposium, CSS 2013 Zhangjiajie, China, November 13-15, 2013 Proceedings
EditorsGuojun Wang, Indrakshi Ray, Dengguo Feng, Muttukrishnan Rajarajan
Place of PublicationCham Switzerland
PublisherSpringer
Pages213-227
Number of pages15
ISBN (Electronic)9783319035840
ISBN (Print)9783319035833
DOIs
Publication statusPublished - 2013
Externally publishedYes
EventInternational Symposium on Cyberspace Safety and Security 2013 - Zhangjiajie, China
Duration: 13 Nov 201315 Nov 2013
Conference number: 5th
https://link.springer.com/book/10.1007/978-3-319-03584-0

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8300
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Symposium on Cyberspace Safety and Security 2013
Abbreviated titleCSS 2013
CountryChina
CityZhangjiajie
Period13/11/1315/11/13
Internet address

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