SIT719 - Analytics for Security and Privacy
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | Nil |
| Corequisite: | Nil |
| Incompatible with: | Nil |
| Study commitment | Students will on average spend 150 hours over the teaching period 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 2 hour online lecture per week, 1 x 1 hour practical experience (workshop) each week. |
| Scheduled learning activities - online | Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 1 hour online practical experience (workshop) each week. |
| Note: | ^GIFT City (India) offering is available to students enrolled at the GIFT City (India) campus only |
Content
The increased size of computer networks has led to extensive generation of data collected for network defence. A need has arisen for security experts that understand how to build analytics that make use of this data in order to detect or prevent attacks. This unit will provide students with the fundamental tools to understand this domain of cyber-security. Students will examine this challenge from multiple perspectives. The unit starts from the basics of building scripts to answer questions of large packet captures as a foundational skillset. Once students are comfortable working with large data sets, they will use this new skill to study several supervised machine learning approaches and apply them to real-world network datasets to build analytics that has been shown to be able to detect various malicious attacks. After becoming comfortable with supervised approaches, students will pivot to examining unsupervised methods for network defence, an important topic, since frequently there are insufficient available examples of malicious behaviour to train good models.
Finally students will study the ethical implications of dealing with large datasets that arise in these contexts by examining privacy attacks that have been developed against large datasets and their associated analytics. All these topics will be explored through scaffolded programming assignments designed to be challenging for a student of any level of programming or mathematical experience. At the end of the unit students will have a solid grounding in how modern analytics work and how they can be applied to network defence.
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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