CYB205 – Machine Learning for Cybersecurity

The ability to detect suspicious activity using analytics is already an important component of a cybersecurity practitioner’s toolkit, but it is poised to become an ever-more important skill in the era of big data. Furthermore, machine learning is likely to play an increasingly significant role in conducting cybersecurity analytics, so it is essential that practitioners understand how machine learning and artificial intelligence work in the context of cybersecurity.

This unit comprises the design of intelligent data analysis systems for cybersecurity, which are commonly deployed to safeguard organisational networks and systems against cyber-attacks. Core concepts include: data surveillance principles, scripting for cybersecurity, machine learning fundamentals, and application of machine learning for cybersecurity. Cybersecurity datasets are introduced, evaluated and classified through the application of popular machine learning techniques that fall into the two broad categories of ‘supervised’ and ‘unsupervised’. The unit also covers fundamentals of adversarial machine learning and cybersecurity control using real-life case studies. 

Learning Outcomes:

• Define and explain fundamental data types stored and transmitted in cybersecurity-enabled ICT systems
• Apply fundamentals of statistical cybersecurity data analytics and intelligent data analytics
• Apply principles of machine learning for analysing cybersecurity datasets for threat detection and prediction

Apply concepts of data analytics to foster an organisational cybersecurity culture in a global context