Detection and classification of conflict flows in SDN using machine learning algorithms

• Main Authors: Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah
• Format: Article
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: QA Mathematics; TJ Mechanical engineering and machinery
• Online Access: https://eprints.ums.edu.my/id/eprint/29969/1/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms-Abstract.pdf; https://eprints.ums.edu.my/id/eprint/29969/2/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms.pdf; https://eprints.ums.edu.my/id/eprint/29969/; https://ieeexplore-ieee-org.ezproxy.ums.edu.my/abstract/document/9433563; https://doi.org/10.1109/ACCESS.2021.3081629
• Institution: Universiti Malaysia Sabah

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN...