Assessment activities must be undertaken at the time indicated in the unit guide. Assessment must be submitted by 11:55 pm on their due date. Should these activities be missed due to illness or misadventure, students may apply for Special Consideration.

Late Assessment Policy 

• 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days.
  • Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.
  • Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.
• After 7 days: Submissions more than 7 days late will receive a mark of 0.
• Extensions:
  • Short Extension: Some assessments are eligible for a short extension. You can only apply for a short extension before the due date.
  • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration.

Need help? Review the Special Consideration page HERE

Assessments where Late Submissions will be accepted 

Assignment, Group project and presentation, and Final examination -- NO, unless special consideration is Granted.

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable, and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through http://connect.mq.edu.au/.

Final examination

Assessment Type ¹: Examination Indicative Time on Task ²: 20 hours Due: Exam Week Weighting: 40%

A 2-hour examination in the exam period.

On successful completion you will be able to:

• Explain the basic concepts and the limitations of Artificial Intelligence.
• Communicate professionally in written and oral form to a range of audiences.
• Analyse the trends of applications of Artificial Intelligence in cyber security.

Assignment

Anomaly Analysis ¹: Indicative Time on Task ²: 25 hours Due: Week 6 (03/04/2026) Weighting: 30%

In this assignment, the student will be given a series of datasets and will be asked to develop an analysis of this data and provide a report. This task aims to be able to identify unusual patterns and abnormal activity using data.

On successful completion you will be able to:

• Detect intrusion in networks and systems by applying tools and techniques revealing abnormal patterns in datasets.
• Communicate professionally in written and oral form to a range of audiences.

Group project and presentation

Major Project ¹: Indicative Time on Task ²: 30 hours Due: Week 12 (29/05/2026) Weighting: 20%

In this assessment task, students as a group will be required to research and evaluate a tool leveraging AI for cyber security purposes. The task also involves a presentation of the findings.

On successful completion you will be able to:

• Detect intrusion in networks and systems by applying tools and techniques revealing abnormal patterns in datasets.
• Communicate professionally in written and oral form to a range of audiences.
• Analyse the trends of applications of Artificial Intelligence in cyber security.

¹ If you need help with your assignment, please contact:

• the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
• the Writing Centre for Academic Skills Support.

² Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Release Dates

• Anomaly Analysis: To be released no later than 6th March.
• Major Project: To be released no later than 24th April. 

Requirements to Pass this Unit

To pass this unit you must:

• Achieve a total mark equal to or greater than 50%

There will be one two-hour lecture each week and one one-hour workshop (starting from Week 1). You can find the time and location information via MQ Timetables. You are expected to participate in both lectures and workshops as they provide complimentary learning activities each week. In workshops, you will write code and perform experiments, and in lectures, we will mainly discuss theories, principles, and methods behind these tools.

Methods of Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to their email address from your university email address.

Textbooks

We do not have a single specific textbook, but will refer to the following texts for your reference during the semester:

• Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press.
• Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
• Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211-407.
• Schütze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39, pp. 234-265). Cambridge: Cambridge University Press.

You will be given readings from these and other sources each week.

Technology Used and Required

We will make use of Python 3 for the analysis of cyber security-related datasets, including a range of modules such as scikit-learn, pandas, numpy, and tensorflow which provide additional features. These can all be installed via the Anaconda Python distribution. We will discuss this environment and the installation process in the first week of classes.

Project Work

A major part of the assessment in this unit is based on a project that you will complete in a group. This will allow you to explore the techniques you are learning from classes in a real-world exercise of applying machine learning to cybersecurity.

Week	Topic
1	Applications of AI for Cyber Security
2	Data Preprocessing and Feature Engineering
3	Regression and Classification
4	Clustering
5	Anomaly Detection
6	Private and Secure Machine Learning
7	Behavioural Biometrics Attacks
8	Vulnerability and Malware Analysis
9	Botnets, DDoS Attacks, and Network Traffic Analysis
10	Traffic Analysis and Phishing URL Detection
11	Mobile Voice Controllable Systems and Their Security
12	Digital Forensics
13	Group Project Presentation

We value student feedback to be able to continually improve the way we offer our units. As such, we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page.

Student feedback from the previous offering of this unit was very positive overall, with students pleased with the clarity around assessment requirements and the level of support from teaching staff. As such, no change to the delivery of the unit is planned, however, we will continue to strive to improve the level of support and the level of student engagement.