Data Analytics for Cyber Security

Unit code: NIT3202 | Study level: Undergraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Footscray Park
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
NIT2202 - Big Data; or
NIT2007 - Intelligent Cloud Systems and Architectures
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

In this unit, you will explore the role of data analytics in cybersecurity, focusing on techniques and frameworks such as anomaly detection, intrusion detection systems, threat intelligence, and risk analysis. You will learn to apply machine learning and big data analytics to detect, mitigate, and respond to cyber threats, gaining hands-on experience in analysing security data to identify vulnerabilities and predict potential intrusions. In addition, you will take ethical considerations in cybersecurity, including data privacy, and the human factors affecting security practices. By the end of this unit, you will be prepared to make data-driven decisions in cybersecurity operations and enterprise risk mitigation.

Learning Outcomes

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

  1. Interpret complex security data to identify patterns, anomalies, and potential cyber threats in diverse organisational contexts;
  2. Integrate appropriate big data frameworks to analyse large-scale security datasets;
  3. Design actionable solutions for automated threat detection and security operations;
  4. Synthesise data-driven insights and strategies with organisational security requirements individually and as part of a team;
  5. Evaluate ethical implications in cybersecurity analytics.

Assessment

For Melbourne campuses

Assessment type: Laboratory Work
|
Grade: 20%
Practical labs with verified live interaction.
Assessment type: Assignment
|
Grade: 40%
Automated threat detection project (Group) with demonstration and oral Q&A.
Assessment type: Case Study
|
Grade: 40%
Scenario-based risk assessment with Q&A verification.

Laboratory Work: Conducted under supervision, students complete real-time data analysis, coding, or modelling tasks. Execution is observed, and outcomes are verified through live checks or oral Q&A to ensure individual performance.

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

Selected readings are provided on VU Collaborate.

REFERENCE TEXTS: Conway D, White J. Machine learning for hackers. " O'Reilly Media, Inc."; 2012 Feb 15. Verma RM, Marchette DJ. Cybersecurity Analytics. CRC Press; 2019 Nov 27.

As part of a course

This unit is studied as part of the following course(s):

Bachelor of Cyber Security
NBCS | Bachelor degree | Duration: 3 years
Bachelor of Data Science
NBDS | Bachelor degree | Duration: 3 years

Refer to the course page for information on how to apply for the course. VU takes care to ensure the accuracy of this unit information, but reserves the right to change or withdraw courses offered at any time. Please check that unit information is current with the Student Contact Centre.

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