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LoSS detection using parameter's adjustment based on second order self-similarity statistical model

This paper analyzes Loss of Self-Similarity (LoSS) detection accuracy using parameter's adjustment which includes different values of sampling level and correlation lag. This is important when considering exact and asymptotic self-similar models concurrently in the self-similarity parameter est...

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Main Authors: Rohani, Mohd. Fo’ad, Maarof, Mohd. Aizaini, Selamat, Ali, Kettani, Houssain
Format: Book Section
Published: Institute of Electrical and Electronics Engineers 2008
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/12627/
http://dx.doi.org/10.1109/ITSIM.2008.4632041
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spelling my.utm.126272017-10-04T03:39:45Z http://eprints.utm.my/id/eprint/12627/ LoSS detection using parameter's adjustment based on second order self-similarity statistical model Rohani, Mohd. Fo’ad Maarof, Mohd. Aizaini Selamat, Ali Kettani, Houssain QA75 Electronic computers. Computer science This paper analyzes Loss of Self-Similarity (LoSS) detection accuracy using parameter's adjustment which includes different values of sampling level and correlation lag. This is important when considering exact and asymptotic self-similar models concurrently in the self-similarity parameter estimation method. Due to the needs of high accuracy and fast estimation, the Optimization Method (OM) based on Second Order Self-similarity (SOSS) statistical model was proposed in the previous works to estimate self-similarity parameter. Consequently, Curve Fitting Error (CFE) value estimated from OM is used to detect LoSS efficiently. This work investigates the effect of the parameter's adjustment for improving the CFE accuracy and estimation time speed. We have tested the method with real Internet traffics simulation that consists of normal and malicious packets traffic. Our simulation results show that LoSS detection accuracy and estimation time can be affected by the chosen of sampling level and correlation lag values. Institute of Electrical and Electronics Engineers 2008 Book Section PeerReviewed Rohani, Mohd. Fo’ad and Maarof, Mohd. Aizaini and Selamat, Ali and Kettani, Houssain (2008) LoSS detection using parameter's adjustment based on second order self-similarity statistical model. In: Proceedings - International Symposium on Information Technology 2008, ITSim. Institute of Electrical and Electronics Engineers, New York, pp. 1913-1919. ISBN 978-142442328-6 http://dx.doi.org/10.1109/ITSIM.2008.4632041 doi:10.1109/ITSIM.2008.4632041
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Rohani, Mohd. Fo’ad
Maarof, Mohd. Aizaini
Selamat, Ali
Kettani, Houssain
LoSS detection using parameter's adjustment based on second order self-similarity statistical model
description This paper analyzes Loss of Self-Similarity (LoSS) detection accuracy using parameter's adjustment which includes different values of sampling level and correlation lag. This is important when considering exact and asymptotic self-similar models concurrently in the self-similarity parameter estimation method. Due to the needs of high accuracy and fast estimation, the Optimization Method (OM) based on Second Order Self-similarity (SOSS) statistical model was proposed in the previous works to estimate self-similarity parameter. Consequently, Curve Fitting Error (CFE) value estimated from OM is used to detect LoSS efficiently. This work investigates the effect of the parameter's adjustment for improving the CFE accuracy and estimation time speed. We have tested the method with real Internet traffics simulation that consists of normal and malicious packets traffic. Our simulation results show that LoSS detection accuracy and estimation time can be affected by the chosen of sampling level and correlation lag values.
format Book Section
author Rohani, Mohd. Fo’ad
Maarof, Mohd. Aizaini
Selamat, Ali
Kettani, Houssain
author_facet Rohani, Mohd. Fo’ad
Maarof, Mohd. Aizaini
Selamat, Ali
Kettani, Houssain
author_sort Rohani, Mohd. Fo’ad
title LoSS detection using parameter's adjustment based on second order self-similarity statistical model
title_short LoSS detection using parameter's adjustment based on second order self-similarity statistical model
title_full LoSS detection using parameter's adjustment based on second order self-similarity statistical model
title_fullStr LoSS detection using parameter's adjustment based on second order self-similarity statistical model
title_full_unstemmed LoSS detection using parameter's adjustment based on second order self-similarity statistical model
title_sort loss detection using parameter's adjustment based on second order self-similarity statistical model
publisher Institute of Electrical and Electronics Engineers
publishDate 2008
url http://eprints.utm.my/id/eprint/12627/
http://dx.doi.org/10.1109/ITSIM.2008.4632041
_version_ 1643646002464292864
score 13.211869

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