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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2007
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| Online Access: | http://eprints.utm.my/5603/ http://eprints.utm.my/5603/1/MohdFoad_Rohani12007_AnIimplementationofLoSSDetection.pdf |
| _version_ | 1848891091943686144 |
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| 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 |
| building | UTeM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T20:52:28Z |
| format | Article |
| id | utm-5603 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:52:28Z |
| publishDate | 2007 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-56032017-11-01T04:17:25Z http://eprints.utm.my/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/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 |
| spellingShingle | QA75 Electronic computers. Computer science Rohani, M.F Maarof, M.A. Selamat, A. Kettani, H. An implementation of LoSS detection using SOSS model |
| title | 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_short | An implementation of LoSS detection using SOSS model |
| title_sort | implementation of loss detection using soss model |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/5603/ http://eprints.utm.my/5603/1/MohdFoad_Rohani12007_AnIimplementationofLoSSDetection.pdf |