42896 Advanced Data Analytics for Cybersecurity
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Credit points: 2 cp
Result type: Grade and marks
Anti-requisite(s): 41180 Data Analytics in Cyber Security
Description
Advanced Data Analytics for Cybersecurity combines big data capabilities with threat intelligence to help detect, analyse and alleviate the insider threats, as well as targeted attacks from external bad actors and persistent cyber threats. It includes a number of IT areas, such as 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 students to the machine learning technologies for cybersecurity and the most common approach to standard process for data analytics. This subject offers practice in the advanced technologies of data analytics in cybersecurity, identifying security risks, threats and vulnerabilities to the corporate computers and networks.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Detect and analyse cyber-attacks using data analytics to determine security robustness. (D.1) |
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Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
Teaching and learning strategies
Microcredential presentation includes weekly synchronous one-hour online workshops facilitated by an expert UTS academic(s) supporting self-study and online (LMS) learning activities. Case studies of real-world business illustrate applications of data analytics techniques in cybersecurity. The workshop sessions focus on hands-on experience in cybersecurity and data analytics tools, and the understanding and interpretation of the results. Regular formative quizzes throughout the semester will allow learners to gauge their progress.
Content (topics)
1. Supervised machine learning for cyber security I: overview of the supervised machine learning algorithms, i.e. Neural networks and similarity learning, and etc.
2. Supervised machine learning for cyber security II: case study of the attacks and vulnerabilities in supervised machine learning technologies.
3. Unsupervised machine learning for cyber security I: overview of the unsupervised machine learning algorithms, i.e. reinforcement learning and federated learning, and etc.
4. Unsupervised machine learning for cyber security II: case study of the attacks and vulnerabilities in unsupervised machine learning technologies.
Assessment
Assessment task 1: Security Analysis Report
| Intent: | Demonstrate evidence of technical skills in evaluating and planning security robustness
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|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Project |
| Groupwork: | Individual |
| Weight: | 100% |
| Length: | 2,500 words |
Minimum requirements
In order to pass the microcredential, a learner must achieve an overall mark of 50% or more.