1. APPLY UNCONVENTIONAL ALGORITHMS TO A REAL-WORLD PROBLEM, CRITICALLY EVALUATE THE ALGORITHMS AND REPORT ON THE EXPECTED EFFICIENCY AND ACCURACY.
Analysis, Problem Solving, Application

2. UNDERSTAND AND CRITICALLY ANALYSE A VARIETY OF CONTEMPORARY TECHNIQUES, TOOLS AND ALGORITHMS USED IN THE CYBERSECURITY DOMAIN.
Knowledge and Understanding,
Learning

3. APPRAISE THE CURRENT TRENDS AND THE USEFULNESS OF USING UNCONVENTIONAL METHODS IN CYBERSECURITY. Analysis, Problem Solving

4. IDENTIFY AND CONTRAST VARIOUS NEW APPROACHES TO POSSIBLY INTRODUCE AN EFFICIENT SOLUTION TO CURRENT COMPUTER SECURITY ISSUES.
Problem Solving, Application

The portfolio will cover all learning outcomes. It will be completed over the entire module with various points where students will submit progress for formative feedback. The assignment is likely to address the latest security approaches and technologies and to get the student to develop practical guidelines and artefacts to demonstrate these (Learning Outcomes 1 to 4).

This module introduces students to contemporary topics in cyber security. The module considers the latest and emerging trends, techniques and tools in the cyber security arena. Therefore, the content of this module may change from time to time. The below are indicative topics -

- Machine learning overview
- Applying machine learning methods to cyber security
- Supervised learning for signature-based detection
- Using machine learning for anomaly detection
- Evolutionary computing for cyber security
- Blockchain technology
- Artificial Intelligence Applications for cyber security
- Artificial Immune Systems

This module uses a mix of learning methods, including lectures, tutorials/labs, independent reading, and discussions.

The lectures explain the concept and the theoretical content that give the student a detailed understanding of the topics. The tutorial/lab sessions will allow the student to carry out practical exercises based on the lectures.

The independent reading and discussion (during the tutorial sessions) will help to acquaint the student with the terminology in the field and current issues associated with the topic.

Alpaydin, E. (2014). Introduction to machine learning.3rd edn. Cambridge (USA): MIT Press. ISBN: 978-0262028189
Chio, C. (2018) Machine Learning and Security. O’Reilly, ISBN: 978-1491979907
Data Protection Act 2018 and GDPR 2018 ISO/IEC/IEEE 29148:2011
ISO 8000-8:2015 Data quality -- Part 8: Information and data quality: Concepts and measuring
Maloof, M. (2011). Machine learning and data mining for computer security. London: Springer. ISBN: 978-1849965446
Tan, Y. (2016). Artificial immune system: Applications in Computer Security. Wiley, ISBN: 978-1119076285

Virtual Machines, Windows and Linux operating systems.

None

This module introduces students to contemporary topics in cyber security, and considers the latest and emerging trends, techniques and tools in the cyber security arena. This can include machine learning and its applications, blockchain technology, and AI applications for cyber security.