COIT20287 - Data Analytics for Cyber Security

General Information

Unit Synopsis

In this unit, you will be able to use state-of-the-art data science tools and techniques to solve cutting-edge cyber security challenges. The frequency of cyber security incidents is rapidly increasing, leading to significant financial and social impacts on individuals and organisations. Therefore, there is a demand for cyber security professionals to be able to use analytics to extract insightful information from incident and event data. This unit will prepare you with advanced knowledge and practical skills in storing and analysis of cyber crime data. You will be able to apply machine learning and deep learning algorithms, as well as industry-leading tools to address cyber crime and respond to security events. You will gain knowledge through real-life case studies, allowing you to predict future cyber security challenges.

Details

Level Postgraduate
Unit Level Not Applicable
Credit Points 6
Student Contribution Band 8
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites

Pre-requisite: N5603 Principles of Data Analytics (NOUP59233)

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

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Residential School No Residential School

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Higher Education Unit Availabilities from Term 1 - 2024

There are no Higher Education availabilities for this unit on or after Term 1 - 2024
Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Assessment Tasks

Assessment Task Weighting
1. Written Assessment 80%
2. Presentation 20%

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University’s Grades and Results Policy for more details of interim results and final grades

Past Exams

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Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Unit learning Outcomes

On successful completion of this unit, you will be able to:

  1. Design methods to capture and store data relevant for cyber security analysis
  2. Justify the selection of analytics tools to identify cyber security threats
  3. Formulate a response to threats identified by the analysis of cyber security data
  4. Analyse data to predict cyber security challenges and recommend mitigation strategies.

The Skills Framework for the Information Age (SFIA) defines skills and competencies of ICT professionals. SFIA is used internationally in job descriptions, role profiles and to describe graduate outcomes. This unit contributes to the following workplace skills as defined by SFIA 7 (the SFIA code is included):

  • Information security (SCTY)
  • Analytics (INAN)
  • Data visualisation (VISL)
  • Data management (DATM)
  • Programming/software development (PROG)
  • Security administration (SCAD)
  • Digital forensics (DGFS)

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Written Assessment
2 - Presentation
Alignment of Graduate Attributes to Learning Outcomes
Advanced Level
Professional Level
Graduate Attributes Learning Outcomes
1 2 3 4
1 - Knowledge
2 - Communication
3 - Cognitive, technical and creative skills
6 - Ethical and Professional Responsibility
Alignment of Assessment Tasks to Graduate Attributes
Advanced Level
Professional Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7 8