SIT326 - Advanced Network Analytics and Forensics
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Online Trimester 2: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | SIT384 |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Study commitment | Students will on average spend 150 hours over the trimester undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
| Scheduled learning activities - campus | 1 x 3 hour seminar per week. |
| Scheduled learning activities - online | Online independent and collaborative learning including 1 x 2 hour online seminar per week, weekly meetings. |
Content
This unit will introduce students to advanced topics in network analytics and forensics. Students will learn to work effectively with the large datasets generated modern networks, as well as utilize supervised and unsupervised machine learning approaches to analytics and forensics. Some planned topics and applications in the analytics and forensics areas are:
- Introduction to working with large networking datasets, with forensics examples such as DDoS datasets, and large log database exploitation.
- Unsupervised methods using graph analytics, with applications such as bot-net detection in large computer networks.
- Supervised methods, such as neural networks and random forests, with applications such as malicious TLS certificate detection, Malware classification, and phishing.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.
Unit Fee Information
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