Machine Learning in Software Defined Networks: Data Collection and Traffic Classification

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Amaral, P (Author) Other; Dinis, J (Author) Other; Pinto, P (Author) Other; Bernardo, L (Author) Other; João Manuel R. S. Tavares (Author) FEUP; Mamede, HS (Author) Other

Conference proceedings International

Title: 2016 IEEE 24TH INTERNATIONAL CONFERENCE ON NETWORK PROTOCOLS (ICNP)

Pages: 1-5

24th IEEE International Conference on Network Protocols (ICNP)

Singapore, SINGAPORE, NOV 08-11, 2016

Indexing

ISI Web of Knowledge - 72 Citations

Scopus - 145 Citations

Other information

Authenticus ID: P-00M-CRE

DOI: 10.1109/icnp.2016.7785327

Abstract (EN): Software Defined Networks (SDNs) provides a separation between the control plane and the forwarding plane of networks. The software implementation of the control plane and the built in data collection mechanisms of the OpenFlow protocol promise to be excellent tools to implement Machine Learning (ML) network control applications. A first step in that direction is to understand the type of data that can be collected in SDNs and how information can be learned from that data. In this work we describe a simple architecture deployed in an enterprise network that gathers traffic data using the OpenFlow protocol. We present the data-sets that can be obtained and show how several ML techniques can be applied to it for traffic classification. The results indicate that high accuracy classification can be obtained with the data-sets using supervised learning.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5