Hybrid based traffic classification of online internet application
Also available in printed version
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | Doctoral thesis |
| اللغة: | الإنجليزية |
| منشور في: |
Universiti Teknologi Malaysia
2025
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://utmik.utm.my/handle/123456789/60131 |
| Abstract | Abstract here |
| _version_ | 1854975100679356416 |
|---|---|
| author | Elkarim, Hamza Awad Hamza Ibrahim |
| author2 | Sulaiman Mohd. Noor, supervisor |
| author_facet | Sulaiman Mohd. Noor, supervisor Elkarim, Hamza Awad Hamza Ibrahim |
| author_sort | Elkarim, Hamza Awad Hamza Ibrahim |
| description | Also available in printed version |
| format | Doctoral thesis |
| id | utm-123456789-60131 |
| institution | Universiti Teknologi Malaysia |
| language | English |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-601312025-08-21T09:34:42Z Hybrid based traffic classification of online internet application Elkarim, Hamza Awad Hamza Ibrahim Sulaiman Mohd. Noor, supervisor Electrical engineering Also available in printed version Internet traffic classification has gained significant attention in the last few years. Even though several classification approaches were proposed by the research community, there still exist many open problems on Internet traffic classification. The unreliability of port and payload based classification methods due to unknown ports and encrypted traffic motivates researchers towards adopting Machine Learning (ML) approach. However, the effect on ML performance when training and testing datasets are from different network environments has not been formally addressed. Hybrid classifier was proposed to overcome the limitation of individual port, statistical, and signatures classifiers. But, these hybrid classifiers final results are only based on any one of the individual classifier results. The issue of live online traffic is also not considered. This research is divided into two main parts. The first part considers the effect of three training and testing dataset scenarios obtained from different network segment on ML performance. The second part proposes hybrid Signature Statistical Port Classifiers (SSPC) that not only integrate, but also synergize the advantages of port, signature and statistical classifier. Both parts use real datasets collected from our campus network. The ML datasets were found to influence the traffic features, causing the classification accuracy to enhance if the training and testing dataset collected from the same network segment. Besides, the false positive will decrease if the training and testing datasets were collected from the same network level. The proposed hybrid classifier was used to classify four Internet application classes i.e. web, FTP, Skype, and online game in two stages, initially offline and later online. In the offline stage, SSPC produces more than 95% classification accuracy which is higher when compared with other individual classifiers. As demonstrated in live online experiments, SSPC achieve more than 91% accuracy; therefore, it is suitable to be used for online classification atiff UTM 229 p. Thesis (Ph.D (Kejuruteraan Elektrik)) - Universiti Teknologi Malaysia, 2014 2025-03-17T06:30:27Z 2025-03-17T06:30:27Z 2014 Doctoral thesis https://utmik.utm.my/handle/123456789/60131 valet-20170430-18147 vital:98479 ENG Closed Access UTM Complete Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Electrical engineering Elkarim, Hamza Awad Hamza Ibrahim Hybrid based traffic classification of online internet application |
| thesis_level | PhD |
| title | Hybrid based traffic classification of online internet application |
| title_full | Hybrid based traffic classification of online internet application |
| title_fullStr | Hybrid based traffic classification of online internet application |
| title_full_unstemmed | Hybrid based traffic classification of online internet application |
| title_short | Hybrid based traffic classification of online internet application |
| title_sort | hybrid based traffic classification of online internet application |
| topic | Electrical engineering |
| url | https://utmik.utm.my/handle/123456789/60131 |
| work_keys_str_mv | AT elkarimhamzaawadhamzaibrahim hybridbasedtrafficclassificationofonlineinternetapplication |