| Title | Intelligent SDN Traffic Classification Using Deep Learning: Deep-SDN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Malik, A., Fréin, R. de, Al-Zeyadi, M., Andreu-Perez, J. |
| Conference Name | 2020 2nd International Conference on Computer Communication and the Internet (ICCCI) |
| Keywords | accurate traffic classification, Big Data, centralised network controller, Computer architecture, computer network management, control plane, data plane, Deep Learning, deep learning model, deep packet inspection, deep packet inspection approaches, deep-SDN, exponential growth, feature extraction, fine-grained network management, high computational cost, Inspection, intelligent SDN traffic classification, Internet, Internet traffic, learning (artificial intelligence), machine learning, network activities, network architecture, network management, network traffic, pattern classification, port-based approaches, Protocols, pubcrawl, Resiliency, resource utilisation, Scalability, SDN, software defined networking, software-defined networking, Software-Defined Networks, telecommunication traffic, Traffic analysis, traffic applications, Traffic classification, Training |
| Abstract | Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control plane from the data plane to result in a centralised network controller that maintains a global view over the whole network on its domain. In this paper, we propose a new deep learning model for software-defined networks that can accurately identify a wide range of traffic applications in a short time, called Deep-SDN. The performance of the proposed model was compared against the state-of-the-art and better results were reported in terms of accuracy, precision, recall, and f-measure. It has been found that 96% as an overall accuracy can be achieved with the proposed model. Based on the obtained results, some further directions are suggested towards achieving further advances in this research area. |
| DOI | 10.1109/ICCCI49374.2020.9145971 |
| Citation Key | malik_intelligent_2020 |
Intelligent SDN Traffic Classification Using Deep Learning: Deep-SDN