Associate Professor Mahsa Baktashmotlagh
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- 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 less theory and more practical Cyber Security applications in the course, so I incorporated their feedback 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 MATH7861)
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
CYBR7001, CYBR7002, CYBR7003 and CRIM7080
Course staff
Course coordinator
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 8-10am - Weeks 1-13
Contact on Wednesdays from 10am-12pm - 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, Paper/ Report/ Annotation | Homeworks | 50% Distributed equally among 5 homeworks (10% each homework) |
26/03/2025 3:00 pm 7/04/2025 3:00 pm 16/04/2025 3:00 pm 28/04/2025 3:00 pm 6/05/2025 3:00 pm |
| Computer Code, Presentation, Project, Reflection |
Final Project
|
50% |
16/06/2025 - 19/06/2025
All students must upload their presentation and python notebook (with notes and explanations) to the blackboard on or before 1pm, 16th of June. |
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, Paper/ Report/ Annotation
- Weight
- 50% Distributed equally among 5 homeworks (10% each homework)
- Due date
26/03/2025 3:00 pm
7/04/2025 3:00 pm
16/04/2025 3:00 pm
28/04/2025 3:00 pm
6/05/2025 3:00 pm
- Learning outcomes
- L01, L02, L03, L04, 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.
Submission guidelines
A solution python notebook should be uploaded on the blackboard at the specified date.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
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.
This course uses a progressive assessment approach where feedback and/or detailed solutions will be released to students 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
- Hurdle
- Identity Verified
- Mode
- Oral, Written
- Category
- Computer Code, Presentation, Project, Reflection
- Weight
- 50%
- Due date
16/06/2025 - 19/06/2025
All students must upload their presentation and python notebook (with notes and explanations) to the blackboard on or before 1pm, 16th of June.
- Learning outcomes
- L02, L03, L05, L06, L07, L08
Task description
The students should define/select a cyber security problem, with the datasets relevant to this problem, and propose appropriate machine learning algorithms that can be applied to the problem. All projects will have a presentation, with a Q&A session afterward. The purpose of this Q&A session is to further verify the student's mastery and understanding of the deliverables. Students must also demonstrate their working project (in Python), during the presentation.
Please note: In accordance with UQ assessment policy, the session will be recorded.
Hurdle requirements
Students must achieve at least 50% 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. Course marks will be rounded to the nearest whole number prior to applying the grade cut-offs.Submission guidelines
Students must upload their presentation and python notebook (with notes and explanations) to the blackboard on or before 1pm, 16th of June. The presentation should have the following information: Introduction (description of the cyber security problem, and the relevant datasets); Background & Related works; Proposed machine learning methods to solve the problem; Analysis and justification behind the proposed approaches; Evaluation, and comparison of the proposed approaches, and Conclusions.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
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.
If you are unable to attend in your allocated slot, please submit an Extension of Assessment Due Date request through my.UQ to apply for a rescheduled assessment date.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. Course grade description: Composite mark < 20% |
| 2 (Fail) | 20 - 46 |
Minimal evidence of achievement of course learning outcomes. Course grade description: 20% <= Composite mark < 47% |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: 47% <= Composite mark < 50% OR (Composite mark >= 50% and Final Project < 50%) |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: 50% <= Composite mark < 65% and Final Project>= 50% |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: 65% <= Composite mark < 75% and Final Project >= 50% |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: 75% <= Composite mark < 85% and Final Project >= 50% |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: Composite mark >= 85% |
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
Find the required and recommended resources for this course 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.
Blackboard: Course material and announcements will appear on Blackboard 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 |
Lecture |
Lecture Series Lectures will be on-campus, and also published on the blackboard. Learning outcomes: L02, L04, L06, L07 |
Practical |
Contact Series Learning outcomes: L01, L02, L03, L04, L05, L06, L07 |
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 - Students Policy and Procedure
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: