Projects:2016s1-102 Classifying Internet Applications and Detecting Malicious Traffic from Network Communications

Project Team

Karl Hornlund

Jason Trann

Supervisors

Assoc Prof Cheng Chew Lim

Dr Hong Gunn Chew

Dr Adriel Cheng (DSTG)

Introduction

The project aims to use machine learning to predict the application class of computer network traffic. In particular, we will explore the usefulness of graph based techniques to extract additional features and provide a simplified model for classification; and, evaluate the classification performance with respect to identifying malicious network traffic.

Objectives

- Implement a supervised machine learning system which utilises NetFlow data and spatial traffic statistics to classify network traffic, as described by Jin et al. [12] [18] [19].

- Achieve an appropriate level of accuracy when benchmarked against previous years’ iterations of the project and verify the results of Jin et al. [18].

- Evaluate the effectiveness of using spatial traffic statistics, in particular with respect to identifying malicious traffic.

- Explore improvements and extensions on the current method prescribed by Jin et al. [12] [18] [19].