Project Proposal – Network Intrusion Detection with Machine Learning

Hayden Dunstan, CSC 466

Overview

As many students in our CSC 446 class discussions have agreed, network security is a top priority as the internet is becoming increasingly popular, and an increasing amount of personal information is being stored in online servers. 

Network intrusion detection is widely accepted as an effective method for dealing with network threats[1], though traditional rule-based IDS systems (like Snort) struggle with new attack patterns. During my project I will investigate the effectiveness of Machine Learning Methods on intrusion detection datasets, and attempt to train a model that can effectively detect advanced threats, in order to gain a better understanding of network attacks, and machine learning.

Project Plan

I plan to use the UNSW-NB15 dataset[2] created by the University of Sydney which contains 9 types of attacks, and 49 features. The set contains over 2 million records, and has conveniently been partitioned into a test set and a training set. 

For the first part of my project, I will design a program that can read pcap files and output their features. In the second part of my project I will train two models,  using Random forests and Neural networks respectively, and then evaluate their performance and show the capabilities of my final model in my final report.

Schedule Dates

  • First Biweekly Update Feb 21
  • Midterm Update Mar 7
  • Third biweekly update Mar 21
  • Final Presentation Apr 4
  • Final Report Apr 11

References

[1] Zhen Yang et. al., “A systematic literature review of methods and datasets for anomaly-based network intrusion detection”, Computers & Security, url: network intrusion detection – an overview | ScienceDirect Topics, accessed: Feb 7, 2025

[2] UNSW-NB15 Dataset, University of Sydney, url: The UNSW-NB15 Dataset | UNSW Research, accessed: Feb 7, 2025