Cyber Security and Privacy

Information on the specialist area Cyber Security and Privacy.

Cyber security is concerned with protecting computer systems and their data against malicious or accidental damage. Protection includes methods for prevention, detection and response. Cyber privacy is concerned with protecting privacy in the digital realm, which includes limiting personal information and protecting it from abuse, even when data is shared with another party. The two topics are inter-connected: finding effective notions of privacy management in the digital realm is increasingly critical for cyber security in society at large.

Students registered in this Specialist Area will have the opportunity to study core courses, and take other matched courses which are connected to security and privacy. These will allow:

  • studying programming languages and formal methods, which are used to provide stronger, more reliable foundations for systems;
  • studying data-driven methods for learning and mining, which are used, for example, for e-crime detection, intrusion detection and malware classification.
  • studying foundational aspects such as programming language semantics, game theory,
  • studying text and language techniques which are useful for uncovering hidden meaning, or providing authentication (or spoofing) mechanisms.

Students are recommended to select at least fifty credit points from these courses, including at least one of the core courses currently on offer. The Computer Security course is optional for students who have studied a basic security course previously. Students are encouraged to undertake a project connected to security and privacy. All courses are subject to availability.


Semester 1 Semester 2
Core Courses


Computer Security (20 credits, level 10)

Introduction to Modern Cryptography

Secure Programming

Main Optional Courses

Automated Reasoning (level 9)

Advances in Programming Languages

Blockchains and Distributed Ledgers

Extreme Computing

Human-Computer Interaction

Introductory Applied Machine Learning (20 credits)

Machine Learning and Pattern Recognition (20 credits)

Performance Modelling

Types and Semantics for Programming Languages

Parallel Programming Languages and Systems

Probabilistic Modelling and Reasoning (20 credits)

Software Architecture, Process, and Management

The Human Factor: Working with Users

Machine Learning Practical (20 credits, full year)

Related links

Informatics sortable course list

Informatics course timetable