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An implementation of LoSS detection using SOSS model

Recent studies have shown that malicious Internet traffic such as Denial of Service (DoS) packets introduces distribution error and perturbs the self-similarity property of network traffic. As a result, Loss of Self-Similarity (LoSS) is detected due to the abnormal traffic packets hence degrading th...

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Main Authors: Rohani, M.F, Maarof, M.A., Selamat, A., Kettani, H.
Format: Article
Language:English
Published: Penerbit UTM Press 2007
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/5603/1/MohdFoad_Rohani12007_AnIimplementationofLoSSDetection.pdf
http://eprints.utm.my/id/eprint/5603/
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spelling my.utm.56032017-11-01T04:17:25Z http://eprints.utm.my/id/eprint/5603/ An implementation of LoSS detection using SOSS model Rohani, M.F Maarof, M.A. Selamat, A. Kettani, H. QA75 Electronic computers. Computer science Recent studies have shown that malicious Internet traffic such as Denial of Service (DoS) packets introduces distribution error and perturbs the self-similarity property of network traffic. As a result, Loss of Self-Similarity (LoSS) is detected due to the abnormal traffic packets hence degrading the Quality of Service (QoS) performance. In order to fulfill the demand for high speed and accuracy for online Internet traffic monitoring, we propose LoSS detection with second order self-similarity statistical (SOSS) model and estimate the self-similarity parameter using the Optimization Method (OM). We test our approach using synthetic and real traffic data. For the former, we use fractional Gaussian noise (fGn) generator, while for the latter we use FSKSMNet simulation dataset. We investigate the behavior of self-similarity property for normal and abnormal traffic packets with different aggregation sampling level (m). The results show that normal Internet activities preserve exact self-similarity property while abnormal traffic perturbs the structure of self-similarity property. The results also demonstrate that fixed m is not sufficient to detect distribution error accurately. Accordingly, we suggest a multi-level aggregation sampling approach to improve the accuracy of LoSS detection. Penerbit UTM Press 2007-12 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/5603/1/MohdFoad_Rohani12007_AnIimplementationofLoSSDetection.pdf Rohani, M.F and Maarof, M.A. and Selamat, A. and Kettani, H. (2007) An implementation of LoSS detection using SOSS model. Jurnal Teknologi Maklumat, 19 (2). pp. 22-34. ISSN 0128-3790
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/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Rohani, M.F
Maarof, M.A.
Selamat, A.
Kettani, H.
An implementation of LoSS detection using SOSS model
description Recent studies have shown that malicious Internet traffic such as Denial of Service (DoS) packets introduces distribution error and perturbs the self-similarity property of network traffic. As a result, Loss of Self-Similarity (LoSS) is detected due to the abnormal traffic packets hence degrading the Quality of Service (QoS) performance. In order to fulfill the demand for high speed and accuracy for online Internet traffic monitoring, we propose LoSS detection with second order self-similarity statistical (SOSS) model and estimate the self-similarity parameter using the Optimization Method (OM). We test our approach using synthetic and real traffic data. For the former, we use fractional Gaussian noise (fGn) generator, while for the latter we use FSKSMNet simulation dataset. We investigate the behavior of self-similarity property for normal and abnormal traffic packets with different aggregation sampling level (m). The results show that normal Internet activities preserve exact self-similarity property while abnormal traffic perturbs the structure of self-similarity property. The results also demonstrate that fixed m is not sufficient to detect distribution error accurately. Accordingly, we suggest a multi-level aggregation sampling approach to improve the accuracy of LoSS detection.
format Article
author Rohani, M.F
Maarof, M.A.
Selamat, A.
Kettani, H.
author_facet Rohani, M.F
Maarof, M.A.
Selamat, A.
Kettani, H.
author_sort Rohani, M.F
title An implementation of LoSS detection using SOSS model
title_short An implementation of LoSS detection using SOSS model
title_full An implementation of LoSS detection using SOSS model
title_fullStr An implementation of LoSS detection using SOSS model
title_full_unstemmed An implementation of LoSS detection using SOSS model
title_sort implementation of loss detection using soss model
publisher Penerbit UTM Press
publishDate 2007
url http://eprints.utm.my/id/eprint/5603/1/MohdFoad_Rohani12007_AnIimplementationofLoSSDetection.pdf
http://eprints.utm.my/id/eprint/5603/
_version_ 1643644366693072896
score 13.211869

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