Cybersecurity and Artificial Intelligence

Core modules:

Scalable Machine Learning

This module will focus on technologies and algorithms that can be applied to data at a very large scale (e.g. population level). From a theoretical perspective it will focus on parallelisation of algorithms and algorithmic approaches such as stochastic gradient descent. There will also be a significant practical element to the module that will focus on approaches to deploying scalable ML in practice such as SPARK, programming languages such as Python/Scala and deployment on high performance computing platforms/clusters.

15 credits

Fundamental Security Properties and Mechanisms

This wide-ranging module covers some fundamental concepts, properties, and mechanisms in security, e.g. identity, authentication, confidentiality, privacy, anonymity, availability and integrity. Cryptographic algorithms are explored together with major attacks (using a break-understand-and-fix approach). High level security protocols are explored (passwords, graphical passwords, key distribution and authentication protocols) together with some rigorous mechanisms for reasoning about their correctness (e.g. belief logics). Other mechanisms such as biometric authentication are also covered.

15 credits

Development of Secure Software

This module covers the security analysis - as well as the secure development - of software-based systems, both on an architectural as well as a system level. The main goal of this module is to teach the foundations of secure software design, secure programming, and security testing. The module requires a solid understanding of software development in general and in particular, of at least one programming language (e.g., Java, JavaScript, Ruby, C#, F#, or C) and basic software development tools such as an IDE (e.g., Eclipse, VS Code), a revision system (e.g., git), or build systems (e.g., Maven, Gradle, npm, FAKE). Moreover, an understanding of database and Web applications is required. The labs require a basic command of Linux in general and the command line (shell) in particular.

15 credits

Cyber Threat Hunting and Digital Forensics

The module provides an in depth view of threat hunting in memory, file system and network data and an introductory analysis of malicious programs. Practical sessions will elaborate on key concepts of incident handling, cyber threat hunting and digital investigation along with detailed analysis of real world case studies. We will also introduce some unusual and non-virulent types of malware.

15 credits

Security of Control and Embedded Systems

This module will explore security issues in systems where computation is carried out to sense, analyse, and control physical system elements. These systems typically react in real time to external events. Examples include washing machines, autonomous vehicles and traffic management systems, power distribution systems, automated manufacturing systems, robotic applications, and web-enabled toys. Many now, or will, operate as part of the 'Internet of Things'. A breach in the security of the systems of interest could also have catastrophic safety consequences. Complications arise when intrusions are detected, e.g., closing down a system may simply not be possible.

15 credits

Text Processing

This module introduces fundamental concepts and ideas in natural language text processing, covers techniques for handling text corpora, and examines representative systems that require the automated processing of large volumes of text. The module focuses on modern quantitative techniques for text analysis and explores important models for representing and acquiring information from texts. You should be aware that there are limited places available on this course. 

15 credits

Machine Learning and Adaptive Intelligence

The module is about core technologies underpinning modern artificial intelligence. The module will introduce statistical machine learning and probabilistic modelling and their application to describing real-world phenomena. The module will give students a grounding in modern state-of-the-art algorithms that allow modern computer systems to learn from data. It has a considerable focus on the mathematical underpinnings of key ML approaches, requiring some knowledge of linear algebra, differentiation and probability.

Students should be aware that there are limited places available on this module.

15 credits

Natural Language Processing

This module provides an introduction to the field of computer processing of written natural language, known as Natural Language Processing (NLP). We will cover standard theories, models and algorithms, discuss competing solutions to problems, describe example systems and applications, and highlight areas of open research. You should be aware that there are limited places available on this module.

15 credits

Cybersecurity and AI Dissertation Project

For your individual project you can choose from a wide range of possibilities in many different environments both within, and outside, the University. The project is completed during the summer and you will have a personal academic supervisor to guide you during this period. The individual project is examined by a dissertation based on the project work and an oral examination. This module has been designed for students taking the MSc Cybersecurity and AI programme; the topic will be at the interface of Cybersecurity and AI. This provides a significant element of glue, bringing together the two areas that make up the programmes.

60 credits