Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Using deep learning to classify community network traffic
Traffic classification is an important aspect of network management. This aspect improves the quality of service, traffic engineering, bandwidth management and internet security. Traffic classification methods continue to evolve due to the ever-changing dynamics of modern computer networks and the t...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Computer Science
2023
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613245504749568 |
|---|---|
| access_status_str | Open Access |
| author | Matowe, Chiratidzo |
| author2 | Chavula, Josiah |
| author_browse | Chavula, Josiah Matowe, Chiratidzo |
| author_facet | Chavula, Josiah Matowe, Chiratidzo |
| author_sort | Matowe, Chiratidzo |
| collection | Thesis |
| description | Traffic classification is an important aspect of network management. This aspect improves the quality of service, traffic engineering, bandwidth management and internet security. Traffic classification methods continue to evolve due to the ever-changing dynamics of modern computer networks and the traffic they generate. Numerous studies on traffic classification make use of the Machine Learning (ML) and single Deep Learning (DL) models. ML classification models are effective to a certain degree. However, studies have shown they record low prediction and accuracy scores. In contrast, the proliferation of various deep learning techniques has recorded higher accuracy in traffic classification. The Deep Learning models have been successful in identifying encrypted network traffic. Furthermore, DL learns new features without the need to do much feature engineering compared to ML or Traditional methods. Traditional methods are inefficient in meeting the demands of ever-changing requirements of networks and network applications. Traditional methods are unfeasible and costly to maintain as they need constant updates to maintain their accuracy. In this study, we carry out a comparative analysis by adopting an ML model (Support Vector Machine) against the DL Models (Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU to classify encrypted internet traffic collected from a community network. In this study, we performed a comparative analysis by adopting an ML model (Support vector machine). Machine against DL models (Convolutional Neural networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU) and to classify encrypted internet traffic that was collected from a community network. The results show that DL models tend to generalise better with the dataset in comparison to ML. Among the deep Learning models, the hybrid model outperformed all the other models in terms of accuracy score. However, the model that had the best accuracy rate was not necessarily the one that took the shortest time when it came to prediction speed considering that it was more complex. Support vector machines outperformed the deep learning models in terms of prediction speed. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37511 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:05.164Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37511 Using deep learning to classify community network traffic Matowe, Chiratidzo Chavula, Josiah computer science Traffic classification is an important aspect of network management. This aspect improves the quality of service, traffic engineering, bandwidth management and internet security. Traffic classification methods continue to evolve due to the ever-changing dynamics of modern computer networks and the traffic they generate. Numerous studies on traffic classification make use of the Machine Learning (ML) and single Deep Learning (DL) models. ML classification models are effective to a certain degree. However, studies have shown they record low prediction and accuracy scores. In contrast, the proliferation of various deep learning techniques has recorded higher accuracy in traffic classification. The Deep Learning models have been successful in identifying encrypted network traffic. Furthermore, DL learns new features without the need to do much feature engineering compared to ML or Traditional methods. Traditional methods are inefficient in meeting the demands of ever-changing requirements of networks and network applications. Traditional methods are unfeasible and costly to maintain as they need constant updates to maintain their accuracy. In this study, we carry out a comparative analysis by adopting an ML model (Support Vector Machine) against the DL Models (Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU to classify encrypted internet traffic collected from a community network. In this study, we performed a comparative analysis by adopting an ML model (Support vector machine). Machine against DL models (Convolutional Neural networks (CNN), Gated Recurrent Unit (GRU) and a hybrid model: CNNGRU) and to classify encrypted internet traffic that was collected from a community network. The results show that DL models tend to generalise better with the dataset in comparison to ML. Among the deep Learning models, the hybrid model outperformed all the other models in terms of accuracy score. However, the model that had the best accuracy rate was not necessarily the one that took the shortest time when it came to prediction speed considering that it was more complex. Support vector machines outperformed the deep learning models in terms of prediction speed. 2023-03-23T07:37:09Z 2023-03-23T07:37:09Z 2022 2023-03-23T07:34:39Z Master Thesis Masters MSc http://hdl.handle.net/11427/37511 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | computer science Matowe, Chiratidzo Using deep learning to classify community network traffic |
| thesis_degree_str | Master's |
| title | Using deep learning to classify community network traffic |
| title_full | Using deep learning to classify community network traffic |
| title_fullStr | Using deep learning to classify community network traffic |
| title_full_unstemmed | Using deep learning to classify community network traffic |
| title_short | Using deep learning to classify community network traffic |
| title_sort | using deep learning to classify community network traffic |
| topic | computer science |
| url | http://hdl.handle.net/11427/37511 |
| work_keys_str_mv | AT matowechiratidzo usingdeeplearningtoclassifycommunitynetworktraffic |