42899 Applied Data Analytics for Cybersecurity
Warning: The information on this page is indicative. The subject outline for a
particular session, location and mode of offering is the authoritative source
of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.
Subject handbook information prior to 2025 is available in the Archives.
Credit points: 2 cp
Result type: Grade and marks
Anti-requisite(s): 41180 Data Analytics in Cyber Security
Description
Applied Data Analytics for Cybersecurity covers 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 introduces a number of new cybersecurity challenges, such as malware analysis, adversarial machine learning, deep learning based anomaly detection, privacy preserving data analytics, etc. Applied Data Analytics for Cybersecurity introduces students to the artificial intelligence technologies for cybersecurity and to the most common approaches to data analytics. This subject offers practice in the applied data analytics of cybersecurity, identifying security risks, threats and vulnerabilities to corporate computers and networks.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Synthesise data analytics with other techniques to appropriately set rules indicators for intrusion detection. (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. Malware Analysis
2. Adversarial machine learning
3. Deep learning based anomaly detection
4. Privacy preserving data analytics
Assessment
Assessment task 1: Intrusion Detection Report
| Intent: | Students demonstrate their knowledge of means for detecting intrusions to corporate computers and network systems |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 100% |
| Length: | 2,000 words |
Minimum requirements
In order to pass the microcredential, a learner must achieve an overall mark of 50% or more.