Universiti Teknologi Malaysia Institutional Repository

Multi-level sampling approach for continous loss detection using iterative window and statistical model

Rohani, Mohd. Fo’ad and Maarof, Mohd. Aizaini and Selamat, Ali and Kettani, Houssain (2010) Multi-level sampling approach for continous loss detection using iterative window and statistical model. International Islamic University Malaysia (IIUM) Engineering Journal, 11 (2). pp. 151-162. ISSN 1511-788X

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Abstract

This paper proposes a Multi-Level Sampling (MLS) approach for continuous Loss of Self-Similarity (LoSS) detection using iterative window. The method defines LoSS based on Second Order Self-Similarity (SOSS) statistical model. The Optimization Method (OM) is used to estimate self-similarity parameter since it is fast and more accurate in comparison with other estimation methods known in the literature. Probability of LoSS detection is introduced to measure continuous LoSS detection performance. The proposed method has been tested with real Internet traffic simulation dataset. The results demonstrate that normal traces have probability of LoSS detection below the threshold at all sampling levels. Meanwhile, false positive detection can occur where abnormal traces have probability of LoSS that imitates normal behavior at sampling levels below 100 ms. However, the LoSS probability exceeds the threshold at sampling levels larger than 100 ms. Our results show the possibility of detecting anomaly traffic behavior based on obtaining continuous LoSS detection monitoring.

Item Type:Article
Uncontrolled Keywords:loss of self-similarity (LoSS), multi-level sampling, anomaly traffic detection, second order self-similarity, iterative window
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:38006
Deposited By:INVALID USER
Deposited On:14 May 2014 14:34
Last Modified:15 Feb 2017 09:43

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