SIT384 - Cyber Security Analytics
Unit details
| Year: | 2022 unit information |
|---|---|
| Important Update: | Unit delivery will be in line with the most current COVIDSafe health guidelines. We continue to tailor learning experiences for each unit to achieve the best possible mix of online and on-campus activities that successfully blend our approaches to learning, working and research. Please check your unit sites for announcements and updates. Last updated: 4 March 2022 |
| 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. |
| Scheduled learning activities - campus: | 2 x 1 hour online class per week, 1 x 2 hour workshop per week. |
| Scheduled learning activities - cloud: | Online independent and collaborative learning including optional scheduled activities as detailed in the unit site. |
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 |
| ULO2 | Apply and explain the principles of data analytics including classification, clustering, regression, supervised learning and unsupervised learning. | GLO1: Discipline-specific knowledge and capabilities |
| 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 |
| ULO4 | Justify meeting specified outcomes through providing relevant evidence and critiquing the quality of that evidence against given criteria. | GLO4: Critical thinking |
These Unit Learning Outcomes are applicable for all teaching periods throughout the year
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Learning portfolio | Python code, Screenshot images, documents, video links | 80% | Week 12 |
| Examination | 2-hour written examination | 20% | Examination period |
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, and must achieve a mark of at least 50% in the examination.
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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