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This microcredential combines big data capabilities with threat intelligence to help detect, analyse and alleviate insider threats and targeted attacks from external bad actors and persistent cyber threats. It covers key topics including statistical methods for identifying patterns in data and making inferences and other intelligent technologies that derive cybersecurity issues from data.
Advanced Data Analytics for Cybersecurity introduces participants to machine learning technologies for cybersecurity and the most common approach to standard process for data analytics. This course offers practice in advanced technologies of data analytics in cybersecurity, identifying security risks, threats, and vulnerabilities to corporate computers and networks.
In this course, you will meet and work with a dedicated course facilitator who will support your learning and engagement with teaching resources designed by the lead academic and team of experts from the Faculty of Engineering and IT.
The course is structured into four modules. Each module comprises self-study materials and facilitated online sessions. The four modules and key topics covered are:
Module 1 - Supervised machine learning for cybersecurity I
This module introduces machine leaning, deep learning and an overview of supervised machine learning algorithms from a cybersecurity perspective.
Module 2 - Supervised machine learning for cybersecurity II
This module presents an in-depth case study of attacks on supervised machine learning technologies, in order to explore their potential vulnerabilities to cyber risks.
Module 3 - Unsupervised machine learning for cybersecurity I
This module provides an overview of unsupervised machine learning algorithms, including reinforcement learning and federated learning, from a cybersecurity perspective.
Module 4 - Unsupervised machine learning for cybersecurity II
This module presents an in-depth case study of attacks on unsupervised machine learning technologies, to explore their potential vulnerabilities to cyber risks.
This course is delivered in a scheduled format over ten weeks.
Each week (during weeks 1 to 8) you will participate in an online session where you will have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.
Weeks nine and ten are planned to give you time to complete the final assignment, with support and scheduled Q&A sessions provided.
This microcredential aligns with the two-credit point subject, Data Analytics in Cyber Security (411180) in one of the following postgraduate offerings:
This microcredential may qualify for recognition of prior learning at this and other institutions.
This microcredential is accessible to professionals from a wide range of sectors and backgrounds who have completed our Data Analytics for Cybersecurity Foundations microcredential, or who otherwise have foundational professional experience in the field.
UTS microcredentials are developed for professionals with the capacity to undertake postgraduate tertiary education.
Upon successful completion of this course, you will be able to detect and analyse cyber-attacks using data analytics.
The graded assessment task is an individually written assessment on spam data analysis. This assignment includes reflection on individual practical work on spam data analysis (pre-processing and transformation) for cybersecurity, with artefacts (screenshots/outputs) of the practical work to be submitted in the report.
Length: 2,500 words
This course includes weekly, live, one-hour online tutorials and one-hour weekly Q&A sessions facilitated by an expert UTS academic, supporting self-study and online learning activities.
Case studies of real-world applications illustrate applications of key tools and techniques. The workshop sessions focus on hands-on activities and examples. Regular formative quizzes throughout the course will allow participants to gauge their progress.
This course is accessible to participants with basic mathematical proficiency (linear algebra and statistics) and some programming experience in Python.
To complete this online course, you will need a personal computer with reliable internet access, web conferencing capability and an operating system with a web browser compatible with the Canvas LMS. You will need to be able to run a currently-supported version of Python.
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START DATE |
05 April |
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MODE |
Online |
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DURATION |
10 wks |
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COMMITMENT |
5 hrs p/w |
Tianqing is an experienced lecturer in cybersecurity, with an extensive background in teaching and research in privacy preserving, cybersecurity and data analytics.
Tianqing’s research interests include designing novel privacy preserving models, developing efficient algorithms and performing in-depth analytics on a wide spectrum of very large, real-world data sets.
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