Security Analytics (COMP90073)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
| Availability | Semester 2 |
|---|---|
| Fees | Look up fees |
AIMS
As we become more dependent on data in every aspect of our lives the task of protecting it and applications dependant on it becomes harder. The sheer quantity of data and sophistication of the attacks is rapidly making manual analysis infeasible. Security Analytics will examine how we can protect data and automate the analysis of data to better detect, predict and prevent privacy and security vulnerabilities.
INDICATIVE CONTENT
The subject will first introduce the types of information leakage that can occur under several threat models and explore methods for protecting sensitive content during data analysis. The second part of the subject will introduce methods from machine learning that are widely used for cyber security analysis. Specific unsupervised machine learning techniques will be covered in more detail, which include methods for anomaly detection, alarm correlation and intrusion detection. The third part of the subject will introduce some of the theoretical challenges and emerging issues for security analytics research, based on recent trends in the evolution of security threats.
Indicative examples of the emerging challenges and issues that will be studied are privacy‐preserving analytics, adversarial machine learning, concept drift and new applications in monitoring critical infrastructure.
Intended learning outcomes
On completion of the subject, students should be able to:
- Evaluate the suitability of different types ofmonitoring data for detecting security incidents
- Describe and implement a range of pattern recognition and machine learning algorithms for use in security analytics
- Select algorithms appropriate to a given security analysis task
- Apply pattern recognition and machine learning techniques to non‐trivial security analysis tasks
- Evaluate computational techniques for security analytics to solve real‐world problems, based on their accuracy and efficiency
- Discuss theoretical challenges and emerging trends for security analytics research
Generic skills
- Ability to undertake problem identification, formulation and solution
- Ability to utilise a systems approach to complex problems
- Capacity for creativity and innovation
- Ability to communicate the results of complex analysis effectively to both technical audiences and the community at large
Last updated: 4 March 2025
Eligibility and requirements
Prerequisites
Students must meet one of the following prerequisite options:
One of
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| No longer available | |||
| COMP30027 | Machine Learning | Semester 1 (On Campus - Parkville) |
12.5 |
| COMP90049 | Introduction to Machine Learning |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The Master of Information Technology welcomes applications from students with disabilities. It is University and degree policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student’s participation in the degree.
The Master of Information Technology requires all students to enrol in subjects where they will require:
- The ability to comprehend complex theory and technology-related information
- The ability to clearly and independently communicate a knowledge and application of theory, and technology principles and practices during assessment tasks
- The ability to actively and safely contribute in IT development and management activities
Students must possess behavioural and social attributes that enable them to participate in a complex learning environment. Students are required to take responsibility for their own participation and learning. They also contribute to the learning of other students in collaborative learning environments, demonstrating interpersonal skills and an understanding of the needs of other students. Assessment may include the outcomes of tasks completed in collaboration with other students.
There may be additional inherent academic requirements for some subjects, and these requirements are listed within the description of the requirements for each of these subjects.
Students who feel their disability will impact on meeting this requirement are encouraged to discuss this matter with the relevant Subject Coordinator and the Disability Liaison Unit: http://www.services.unimelb.edu.au/disability/
Last updated: 4 March 2025
Assessment
| Description | Timing | Percentage |
|---|---|---|
Programing and research-based project
| Week 5 | 15% |
Programing and research-based project
| Week 11 | 25% |
One 2 hour end of semester written examination.
| During the examination period | 60% |
Last updated: 4 March 2025
Dates & times
- Semester 2
Principal coordinator Sarah Monazam Erfani Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1 hour lectures and 1 tutorial per week Total time commitment 200 hours Teaching period 28 July 2025 to 26 October 2025 Last self-enrol date 8 August 2025 Census date 1 September 2025 Last date to withdraw without fail 26 September 2025 Assessment period ends 21 November 2025 Semester 2 contact information
Sarah Monazam Erfani
sarah.erfani@unimelb.edu.au
Time commitment details
200 hours
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 4 March 2025
Further information
- Texts
- Subject notes
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Data Science Course Master of Computer Science Course Master of Information Technology Course Master of Engineering Specialisation (formal) Software - Available to Study Abroad and/or Study Exchange Students
Last updated: 4 March 2025