SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models
Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidt...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/8/6/201 |
| _version_ | 1797531338645962752 |
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| author | Elaiyasuriyan Ganesan I-Shyan Hwang Andrew Tanny Liem Mohammad Syuhaimi Ab-Rahman |
| author_facet | Elaiyasuriyan Ganesan I-Shyan Hwang Andrew Tanny Liem Mohammad Syuhaimi Ab-Rahman |
| author_sort | Elaiyasuriyan Ganesan |
| collection | DOAJ |
| description | Due to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques. |
| first_indexed | 2024-03-10T10:42:22Z |
| format | Article |
| id | doaj.art-cdaf2d8d57d146508ec289ed9709d2ce |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-10T10:42:22Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-cdaf2d8d57d146508ec289ed9709d2ce2023-11-21T22:51:37ZengMDPI AGPhotonics2304-67322021-06-018620110.3390/photonics8060201SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML ModelsElaiyasuriyan Ganesan0I-Shyan Hwang1Andrew Tanny Liem2Mohammad Syuhaimi Ab-Rahman3Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, TaiwanDepartment of Computer Science and Engineering, Yuan Ze University, Taoyuan 32003, TaiwanDepartment of Computer Science, Universitas Klabat Manado, North Sulawesi 95371, IndonesiaDepartment of Electrical, Electronics, and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, MalaysiaDue to the rapid growth of the Internet of Things (IoT), applications such as the Augmented Reality (AR)/Virtual Reality (VR), higher resolution media stream, automatic vehicle driving, the smart environment and intelligent e-health applications, increasing demands for high data rates, high bandwidth, low latency, and the quality of services are increasing every day (QoS). The management of network resources for IoT service provisioning is a major issue in modern communication. A possible solution to this issue is the use of the integrated fiber-wireless (FiWi) access network. In addition, dynamic and efficient network configurations can be achieved through software-defined networking (SDN), an innovative and programmable networking architecture enabling machine learning (ML) to automate networks. This paper, we propose a machine learning supervised network traffic classification scheduling model in SDN enhanced-FiWi-IoT that can intelligently learn and guarantee traffic based on its QoS requirements (QoS-Mapping). We capture the different IoT and non-IoT device network traffic trace files based on the traffic flow and analyze the traffic traces to extract statistical attributes (port source and destination, IP address, etc.). We develop a robust IoT device classification process module framework, using these network-level attributes to classify IoT and non-IoT devices. We tested the proposed classification process module in 21 IoT/Non-IoT devices with different ML algorithms and the results showed that classification can achieve a Random Forest classifier with 99% accuracy as compared to other techniques.https://www.mdpi.com/2304-6732/8/6/201SDN-FiWi-IoTQoS-mappingnetwork traffic classificationmachine learning |
| spellingShingle | Elaiyasuriyan Ganesan I-Shyan Hwang Andrew Tanny Liem Mohammad Syuhaimi Ab-Rahman SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models Photonics SDN-FiWi-IoT QoS-mapping network traffic classification machine learning |
| title | SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models |
| title_full | SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models |
| title_fullStr | SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models |
| title_full_unstemmed | SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models |
| title_short | SDN-Enabled FiWi-IoT Smart Environment Network Traffic Classification Using Supervised ML Models |
| title_sort | sdn enabled fiwi iot smart environment network traffic classification using supervised ml models |
| topic | SDN-FiWi-IoT QoS-mapping network traffic classification machine learning |
| url | https://www.mdpi.com/2304-6732/8/6/201 |
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