NDLI: Network traffic self similarity measurements using classifier based Hurst parameter estimation

Content Provider	IEEE Xplore Digital Library
Author	Premarathne, U. Premaratne, U. Samarasinghe, K.
Copyright Year	2010
Description	Author affiliation: Department of Electronic and Telecommunication Engineering, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka (Premaratne, U.; Samarasinghe, K.) \|\| IS and VAS - Engineering Division, SLT VisionCom PVT LTD, Colombo 04, Sri Lanka (Premarathne, U.)
Abstract	Network traffic has been shown on numerous occasions to be self similar under normal conditions. This self similar property is however, lost during anomalous conditions such as device failure, congestion and malicious intrusions. Therefore, this loss of self similarity can be used to detect such events. The Hurst parameter (H) is the most widely accepted parameter for determining self similarity. However, an accurate estimate is data and computationally expensive. This paper discusses the potential of using efficient classifier and soft computing based approaches for determining self similarity. Traffic data is obtained for various user activities from genuine browsing to malicious attacks. This data is then analysed for self similarity. The logarithmic normalized histogram of the packet interarrival time is used to obtain a feature set for classification. Various techniques are used to analyse and reduce the feature set. Classification is done using Naive Bayes classifiers and Support Vector Machines (SVM). Artificial Neural Networks (ANN) are also used to estimate the Hurst parameter using function approximation. The results show that classifiers can detect non self similar behaviour with a very high accuracy of up to 100%.
Starting Page	64
Ending Page	69
File Size	209828
Page Count	6
File Format	PDF
ISBN	9781424485499
e-ISBN	9781424485529
DOI	10.1109/ICIAFS.2010.5715636
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-17
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wireless communication Network traffic self similarity Histograms Estimation Artificial neural networks Hurst parameter classifiers Function approximation packet interarrival histogram Kernel
Content Type	Text
Resource Type	Article

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