| Semester 1, 2022 Springfield On-campus | |
| Units : | 1 |
| Faculty or Section : | Faculty of Business, Education, Law and Arts |
| School or Department : | School of Business |
| Grading basis : | Graded |
| Course fee schedule : | /current-students/administration/fees/fee-schedules |
Staffing
Examiner:
Overview
Detecting and preventing cyber-attacks becomes increasingly important in order to protect businesses from cyber terrorism threats. Data mining techniques can be effective in tackling cyber security challenges. This course provides critical knowledge required to understand the application of data mining to cyber threats in order to protect businesses from any unforeseen cyber-attacks.
This course provides an overview of the different types of cyber-attacks, the business systems that are most at risk, and the strengths and challenges of data mining approach to cybersecurity. The course will cover data mining algorithms including prediction, classification, clustering mechanisms, and association rules, which have all been used to discover and generalize attack patterns so as to develop powerful business solutions for dealing with the threats. Students will also learn various applications of data mining that can be utilised in the real-time detection of threats. The course also provides an opportunity for hands-on experimentation with applying data mining to real-life security problems in the practical workshop with real-world data.
Course learning outcomes
On successful completion of this course students should be able to:
- examine the processes associated with the use of data mining tools in the context of cybersecurity;
- synthesise and articulate a wide range of cybersecurity applications to data mining;
- critically assess data mining & data analytics to address business process threats as a result of cyberattack;
- make informed decisions based on high level knowledge and skills about data mining techniques to realworld cybersecurity applications;
- analyse the strengths and challenges of data mining techniques to detect cyber security incidents in business environments.
Topics
| Description | Weighting(%) | |
|---|---|---|
| 1. | Introduction to data mining and analytics for cyber security | 10.00 |
| 2. | Data mining techniques | 20.00 |
| 3. | Network and system security | 10.00 |
| 4. | Data mining for malware and intrusion and fraud detection | 20.00 |
| 5. | Social network security | 10.00 |
| 6. | Email: Spam detection, Phishing detection | 15.00 |
| 7. | Internet of Things (IoT)/Infrastructure security | 15.00 |
Text and materials required to be purchased or accessed
Student workload expectations
To do well in this subject, students are expected to commit approximately 10 hours per week including class contact hours, independent study, and all assessment tasks. If you are undertaking additional activities, which may include placements and residential schools, the weekly workload hours may vary.
Assessment details
| Description | Weighting (%) |
|---|---|
| ASSIGNMENT 1 | 10 |
| ASSIGNMENT 2 | 25 |
| ASSIGNMENT 3 | 25 |
| ONLINE EXAMINATION | 40 |