SIT384 - Cyber Security Analytics

Unit details

Year:

2024 unit information

Enrolment modes:Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Online
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Shang Gao
Prerequisite:

SIT102 and SIT182

Corequisite:Nil
Incompatible with: Nil
Typical 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.

Educator-facilitated (scheduled) learning activities - on-campus unit enrolment:

2 x 1 hour online lectures per week, 1 x 2 hour practical experiences (workshop) per week.

Educator-facilitated (scheduled) learning activities - online unit enrolment:

Online independent and collaborative learning including 2 x 1 hour online lectures per week (recordings provided), 1 x 2 hour online practical experiences (workshop) per week.

Content

In SIT384 students will learn about the various data analytical methodologies used to investigate cyber security problems. In particular, we will focus on processing and analysing data relevant to cyber security systems and applications. You will be introduced to the scripting techniques and solutions required for data analytics in the context of cyber security. Applying appropriate data analytical methods and solving cyber security problems will be a key practical element of this unit.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes
ULO1 Identify common formats of data stored and transmitted in the context of cyber security systems and applications.  GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO4: Critical thinking

ULO2

Apply and explain the principles of data analytics including classification, clustering, regression supervised learning and unsupervised learning. GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication

ULO3

Implement and test small data analytics solutions to process cyber security data using scripting languages such as Python. GLO1: Discipline-specific knowledge and capabilities
GLO5: Problem solving

ULO4

Justify meeting specified outcomes through providing relevant evidence and critiquing the quality of that evidence against given criteria. GLO4: Critical thinking
GLO6: Self-management

Assessment

Assessment Description Student output Grading and weighting
(% total mark for unit)
Indicative due week
Learning portfolio Python code, Screenshot images, documents, video links 100% Week 12

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Hurdle requirement

To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.

Learning Resource

Prescribed text(s): Müller and Guido, 2017, Introduction to Machine Learning with Python: A Guide for Data Scientists, 1st Ed, O'Reilly Media.

The texts and reading list for the unit can be found on the University Library via the link below: SIT384 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

Unit Fee Information

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