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This course introduces 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 comprises several IT areas, including data analytics methods for identifying security issues in data, packet analysis for insider threats, network package and DDoS attack analysis from external threats and other intelligent technologies that derive cybersecurity issues from data.
Data Analytics for Cybersecurity Foundations introduces participants to the significance and language of data analytics in cybersecurity and the most common approach to standard process for data analytics. It offers practice in the foundations of data analytics of cybersecurity - identifying security risks, threats and vulnerabilities to the corporate computer and networks.
During this course, you will meet and work with a dedicated course facilitator who will support your learning and engagement with the teaching resources designed by the lead academic and team of experts, in the Faculty of Engineering and IT.
This course is structured into four modules. Each module comprises self-study materials and facilitated online sessions. The modules and key topics covered are:
This module introduces cybersecurity and features an introduction to key cybersecurity models and attack types and the value of applying data analytics in a cyber security context.
This module introduces data analysis tools for cybersecurity using Python and features programming tutorials, key libraries including NumPy and Pandas and an introduction to data visualisation with Matplotlib.
This module covers network characteristics, OSI 7-layer models, TCP/IP, packet analysis and packet sniffers and a case study of insider threats.
This module introduces and evaluates network packet analysis tools, different types of DDoS attacks and detection and response options and a case study of an external attack.
The 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 suitable for professionals from a wide range of sectors and backgrounds who are new to the field of data analytics and cybersecurity.
UTS microcredentials are developed for professionals with a capacity to undertake postgraduate tertiary education.
Upon successful completion of this course, you will be able to apply data analytics to investigate cybersecurity datasets.
The graded assessment task is an individually written assessment on data exploration and analysis. This assignment includes reflection on individual practical work on data exploration and analysis (pre-processing and transformation) for cybersecurity, with artefacts (screenshots/outputs) of the practical work to be submitted in the report.
Length: 2,000 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 microcredential will allow participants to gauge their progress.
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.
|5 hrs p/w|
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