Universiti Teknologi Malaysia Institutional Repository

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

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

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/ITSIM.2008.4632041

Abstract

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.

Item Type:Book Section
Additional Information:ISBN: 978-142442328-6; International Symposium on Information Technology 2008, ITSim; Kuala Lumpur; 26 August 2008 through 29 August 2008
Uncontrolled Keywords:curve fitting, information technology, model structures, numerical analysis, parameter estimation
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:12627
Deposited By: Liza Porijo
Deposited On:15 Jun 2011 01:46
Last Modified:15 Jun 2011 01:46

Repository Staff Only: item control page