Network traffic classification using machine learning for software-defined networks | |
| Author | Perera, Jayasuriya Kuranage Menuka |
| Call Number | AIT Thesis no.TC-19-06 |
| Subject(s) | Machine learning Computer networks Telecommunication--Traffic |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Telecommunications |
| Publisher | Asian Institute of Technology |
| Abstract | The recent development in industry automation and connected devices made a high demand for network resources. Traditional networks are becoming less effective to handle this large number of traffic generated by these technologies. Software-defined networking introduced a programmable and scalable networking solution that enables machine learning applications to automate these networks. Issues with traditional methods to classify network traffic and allocate resources can be replaced by a new solution. Network data gathered by the SDN controller, which allows data analytics methods to analyze and apply ML models to cus- tomize the network management. This research is focused on analyzing network data and implement a network traffic classification solution using machine learning and integrate that model with software-defined networking. |
| Year | 2019 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Telecommunications (TC) |
| Chairperson(s) | Attaphongse Taparugssanagorn |
| Examination Committee(s) | Teerapat Sanguankotchakorn;Poompat Saengudomlert |
| Scholarship Donor(s) | Asian Institute of Technology Fellowship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2019 |