| dc.contributor.author | Ainomugisha, Edina | |
| dc.date.accessioned | 2021-04-26T14:34:38Z | |
| dc.date.available | 2021-04-26T14:34:38Z | |
| dc.date.issued | 2020-12 | |
| dc.identifier.citation | Ainomugisha, E. (2020).A machine learning approach to traffic classification for reliable computer communication.(Unpublished undergraduate dissertation). Makerere University, Kampala, Uganda. | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.12281/10365 | |
| dc.description | A report submitted in partial fulfillment of the requirements for the Degree of Bachelor of Science in Electrical Engineering at Makerere University | en_US |
| dc.description.abstract | Traffic classification with accuracy is of great importance in network activities for example in security monitoring, quality of service, accounting of network usage and fault detection. Network traffic classification has been significant in the recent years due to the rapid growth in the number of internet users. Software Defined networks is a newly developing technology which is capable of addressing problems in the traditional networks by simplifying network management, introducing network program ability and providing a global view of the network. In recent years, SDN has brought new opportunities to classify data. This project aims at classifying real time traffic using both supervised and unsupervised machine learning algorithms over a Software Defined Network | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Makerere University | en_US |
| dc.subject | Traffic classification, Software Defined Networks | en_US |
| dc.title | A machine learning approach to traffic classification for reliable computer communication | en_US |
| dc.type | Other | en_US |
