Course coordinator
Associate Professor Mahsa Baktashmotlagh
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Students will learn about preparing the data for machine learning, common machine learning techniques and tools (supervised learning, unsupervised learning, deep learning, and large-scale data analysis), and their applications to cybersecurity such as detecting anomalies, detecting known types of attacks like injections, clustering user activities, adversarial learning, etc.
The field of cyber security has grown rapidly in the last few decades, assisted by increases in computational power, and it continues to grow in importance as technologies such as machine learning and high-performance computing make it possible to implement efficient algorithms for cyber security. Artificial Intelligence for Cyber Security is a multidisciplinary course drawing from cyber security, statistics, information theory, and optimization, and it requires basic knowledge of linear algebra, statistics, software development, and cyber security.
Previous cohort preferred adding self-assessment slides in the lectures, which was incorporated in the course.
Course requirements
Assumed background
Prior programming experience in Python, with a basic understanding of probability theory and linear algebra (vector/matrix operations, vector spaces, and norms) is assumed.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(CSSE1001 or CSSE7030 or ENGG1001) and (MATH1061 or MATH1081 or MATH7861)
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
CYBR7001, CYBR7002, CYBR7003 and CRIM7080
Course contact
Course staff
Lecturer
Associate Professor Mahsa Baktashmotlagh
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures on Mondays from 9am-11am (Weeks 1-13)
Applied Classes on Wednesdays (Weeks 2-13)
Aims and outcomes
Artificial Intelligenceﾠfor Cyber Security is a multidisciplinary course drawing from cyber security, statistics, information theory, and optimization. Big hi-tech companies such as Microsoft, Google, Yahoo!, Facebook, IBM, and Amazon are extensively using machine learning in their product development projects to gain a competitive advantage in their services. These companies are increasingly looking for engineers with the knowledge and skills in AI to boost their leading-edge projects. COMP7710 is an integral component of the Master of Cyber Security, and its goal is to build enough understanding of AI algorithms and their practical aspects in the area of cyber security so that an appropriate algorithm can be selected for a given cyber security problem. The students should develop an understanding of:
1. Basic machine learning techniques, e.g. regression, classification, clustering, dimensionality reduction, deep learning;
2. How to apply machine learning models to a given problem;
3.ﾠHow toﾠassess the quality of the machine learningﾠmodel, and select aﾠproper model through cross-validation;
4. Broad understanding of different cyber security tasks/problems, for which, a machine learning technique can be applied;
5. How to select an appropriate machine learning algorithm for a given cyber security problem;
6. Understand, identify, and evaluate the limitations and risks associated with applying machine learning algorithms to cyber security;
7. Effective way of communicating, presenting, and demonstrating a project to an audience.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Demonstrate technical knowledge of the underlying principles and concepts of machine learning algorithms, such as linear/non-linear models for classification and regression, dimensionality reduction, clustering, anomaly detection, and deep learning.
LO2.
Analyze and assess the quality, effectiveness, and robustness of a machine learning model.
LO3.
Demonstrate a broad understanding of common cyber security tasks (e.g., detecting threats, predicting attacks).
LO4.
Understand the practical aspects of machine learning techniques in the area of cyber security.
LO5.
Design, analyze, and evaluate an appropriate machine learning method for a given cyber security problem.
LO6.
Analyze adversarial capabilities and goals, and develop machine learning algorithms to counteract the attacks.
LO7.
Understand the limitations and risks of applying machine learning algorithms to cyber security problems.
LO8.
Organizing and conveying acquired knowledge clearly in both written and spoken forms.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Notebook/ Logbook | Homeworks | 50% (10% each) |
26/03/2026 3:00 pm 13/04/2026 3:00 pm 20/04/2026 3:00 pm 27/04/2026 3:00 pm 5/05/2026 3:00 pm
Please note: Homework 1 is due on Thursday of Week 5, and marks/written feedback will be released by Monday 30 March (prior to Census). |
| Computer Code, Paper/ Report/ Annotation, Notebook/ Logbook, Project |
Final Project - Report
|
20% |
8/06/2026 3:00 pm |
| Project, Reflection |
Final Project - Q&A
|
30% |
Exam week 2 Mon - Exam week 2 Thu |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Homeworks
- Mode
- Written
- Category
- Computer Code, Notebook/ Logbook
- Weight
- 50% (10% each)
- Due date
26/03/2026 3:00 pm
13/04/2026 3:00 pm
20/04/2026 3:00 pm
27/04/2026 3:00 pm
5/05/2026 3:00 pm
Please note: Homework 1 is due on Thursday of Week 5, and marks/written feedback will be released by Monday 30 March (prior to Census).
- Learning outcomes
- L01, L02, L04, L05, L07
Task description
The homework is a short collection of problem-solving exercises where you will demonstrate your ability to understand machine learning algorithms and implement them, broadly understand cyber security problems, and use the appropriate machine learning algorithm to solve cyber security problems.
This is an individually assessed assessment.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
A solution python notebook should be uploaded on the uqlearn at the specified date (UQ Blackboard Online Submission).
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Extensions are limited to 7 days as feedback will be provided within 14 days.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Project - Report
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Notebook/ Logbook, Project
- Weight
- 20%
- Due date
8/06/2026 3:00 pm
- Other conditions
- Student specific.
- Learning outcomes
- L01, L02, L03, L04, L05, L06, L07, L08
Task description
The students work on a cyber security problem, and propose appropriate machine learning algorithms that can be applied to the problem. Students must demonstrate their working project (in Python). Powerpoint slides and the solution python notebook, including the results and explanations, should be uploaded on the blackboard at the specified date.
Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Students must upload their powerpoint slides and python notebook (with notes and explanations) to the blackboard on or before 3pm, 8th of June. The slides should have the following information: 1-slide for Introduction (description of the cyber security problem, and the relevant datasets); 1-slide for Background & Related works; 1-slide for Proposed machine learning methods to solve the problem; 1-slide for Analysis and justification behind the proposed approaches; 1-slide for Evaluation, 1-slide for comparison of the proposed approaches, and 1-slide for Conclusions/Reflection.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
If you are approved an extension for 7 days or more for the 'Final Project - Report', you will automatically receive an extension on the Q&A. Please contact the Course Teaching Team to reschedule your Q&A.
If you are approved an extension for less than 7 days for the 'Final Project - Report', you will need to attend your originally scheduled Q&A.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Project - Q&A
- Hurdle
- Identity Verified
- Mode
- Oral
- Category
- Project, Reflection
- Weight
- 30%
- Due date
Exam week 2 Mon - Exam week 2 Thu
- Other conditions
- Secure.
- Learning outcomes
- L01, L03, L04, L06, L07, L08
Task description
This is a project Q&A session with the purpose of further verifying the student's mastery and understanding of the delivered project and the course content.
Please note: In accordance with UQ assessment policy, the session will be recorded.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Hurdle requirements
Students must achieve at least 40% (12) in this assessment item to pass this course. Otherwise, the overall mark will be capped at 49, corresponding to an overall grade of 3 or lower. Please refer to course grading information.Submission guidelines
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
If you are unable to attend your allocated slot, one reschedule is permitted. If you do not attend the rescheduled session, the mark for this assessment item will be 0.
To apply for a reschedule, you need to apply for an extension via my.UQ.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Consistent with industry practice around presentations to clients/industry partners, no late submissions will be accepted and a 100% late penalty applies. This has been approved by the Associate Dean (Academic)
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Marks | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: Students must achieve at least 40% (12) in the Final Project - Q&A to achieve a grade of 4. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Course marks will be rounded to the nearest whole number prior to applying the grade cut-offs.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help and speak to the course teaching staff. If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help; for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things). Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Additional learning resources information
Facilities: There will be no special access to a computer lab for this course. Please see the ITEE Student Guide for information on expectations and requirements for the use of the computer labs.
UQLearn: Course material and announcements will appear at https://learn.uq.edu.au/. The Blackboard website should be checked for announcements regularly; at least once a week and more often in the week before assignments are due.
Note that solutions or partial solutions to individual assignments and the project should not be posted on any public forum. If you are uncertain about whether or not a post is appropriate, please contact the teaching staff for clarification.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks From Week 1 To Week 13 |
Lecture |
Lecture Series Lectures will be on-campus, and also published on the blackboard. Learning outcomes: L02, L04, L06, L07 |
Multiple weeks From Week 2 To Week 13 |
Applied Class |
Applied Class Series Learning outcomes: L01, L02, L03, L04, L05, L06, L07, L08 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: