NDLI: Intelligent optical network traffic monitor design

Content Provider	IEEE Xplore Digital Library
Author	Mo, Y.K. Leeson, M.S. Green, R.J.
Copyright Year	2012
Description	Author affiliation: School of Engineering University of Warwick Coventry CV4 7AL United Kingdom (Mo, Y.K.; Leeson, M.S.; Green, R.J.)
Abstract	The characteristics of a ground-to-ground radar link maintenance protocol for airfield communications are investigated. Such links are typically made at high speed over optical fibre in modern airfields and an appropriate method to identify both known traffic and unauthorised use is extremely important. Here, the pattern recognition technique of Support Vector Machines (SVMs) is employed for classification, recognising trends by supervised learning and using them to identify anomalies. The work is motive by the EU SESAR project which is developing a unified digital communication platform for air traffic management. To achieve this, it is necessary to gather network information and present it in segments. Packet transmission possesses two key parameters, packet payload and the time interval between transmissions. A simulation including these elements was performed, and SVM employed to search for traffic patterns. This was successful in that two known patterns were identified using a nonlinear polynomial SVM kernel. It is thus shown that it is possible to monitor discrete statistical traffic flows using SVM. Further payload types could be classified as anomalies enabling a significant reduction in the workload of the network engineer to monitor a critical optical network.
Starting Page	1
Ending Page	4
File Size	349361
Page Count	4
File Format	PDF
ISBN	9781467322287
ISSN	21612064
e-ISBN	9781467322294
e-ISBN	9781467322270
DOI	10.1109/ICTON.2012.6253878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Payloads Support vector machines Pattern recognition Training Monitoring Protocols Optical fiber networks Pattern Recognition Network Monitoring Support Vector Machines Intelligent Systems
Content Type	Text
Resource Type	Article

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