AI-Driven Secure Networking
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
NIT2222 - Networking Technologies
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
Gain the practical skills to secure and optimise intelligent networks against evolving threats using AI-assisted technologies. In this unit, you will evaluate proactive defence strategies, advanced threat analysis methods, design and implementcryptographic solutions and intrusion detection prevention systems (IDPS). AI tools will support you in automating analysis and responding proactively to risks, while you design secure, scalable network infrastructures. By the end of the unit, you will be able to evaluate, implement, and optimise AI-driven security solutions: equipping you with the skills required for careers in secure network design, AI-enabled cyber defence, and infrastructure optimisation.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Critically evaluate advanced threat intelligence and analysis techniques in the context of AI-assisted security operations.
- Design and implement advanced cryptographic solutions for securing network communications and protecting data assets.
- Implement, troubleshoot, and optimise Intrusion Detection and Prevention Systems (IDPS) within AI-enabled network environments.
- Construct secure and resilient network infrastructures leveraging AI-driven tools to address complex security threats.
- Critically review and assess ethical and legal considerations in deploying AI-driven defense solutions.
Assessment
For Melbourne campuses
Assessment type: Laboratory Work | Grade: 20%
Practical lab work.
Assessment type: Assignment | Grade: 45%
Assignment on intelligent proactive defense design and implementations with demonstration and Q&A verification.
Assessment type: Case Study | Grade: 35%
Case study on scenario-based problems with verified live interaction.
Assignment:
Includes structured milestone reviews and in-class progress checks. Individual contributions are logged, validated, and justified through recorded peer review or demonstration to ensure authenticity.
Case Study:
Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding.
Required reading
Nil.
As part of a course
This unit is studied as part of the following course(s):