Projects:2017s1-101 Classifying Network Traffic Flows with Deep-Learning

Project Team

Clinton Page

Daniel Smit

Kyle Millar

Supervisors

Dr Cheng-Chew Lim

Dr Hong-Gunn Chew

Dr Adriel Cheng (DST Group)

Introduction

The internet has become a key facilitator of large-scale global communications and is vital in providing an immeasurable number of services every day. With the ever-expanding growth of internet use, it is critical to effectively manage the underlying networks that hold it together. Network traffic classification plays a crucial role in this management, providing quality of service, forecasting future trends, and detecting potential security threats. For these reasons, accurate network traffic classification is of great interest to internet service providers (ISPs), large-scale enterprise companies, and government agencies alike.

Current methods of network traffic classification have become less effective in recent years due to the increasing trend of obscuring network activity, whether it be for security, priority, or malicious intent [1-3]. Therefore, in today’s network there arises a need for a more effective classification algorithm to handle these conditions.

Objectives

• Gain knowledge about the application of deep-learning for classifying network traffic flows

• Conduct experiments on synthetic traffic flows and/or make use of communications flow data from real-life enterprise networks

• Develop network traffic classifying software using deep-learning techniques to an acceptable accuracy when comparing against the results of previous years

References

[1] Z. Wang, "The Applications of Deep Learning on Traffic Identification," Black Hat USA, 2015.

[2] S. Zeba and D.G. Harkut, "An overview of network traffic classification methods," International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), no. ISSN: 2321-8169, pp. 482 - 488, February 2015.

[3] T. Auld, A. W. Moore, and S. F. Gull, "Bayesian neural networks for internet traffic classification," IEEE Transactions on Neural Networks, vol. 18, no. 1, pp. 223-39, Jan 2007.