MSc Cyber Security Analytics

COMM108: Data Systems

This module will introduce you to the ways in which data is stored within a computer system. You will learn about a variety of types of database, including those based on the structured query language (SQL) and those designed to structure data differently (so-called NoSQL databases). You will develop a theoretical understanding about how data should be organised, and will learn how to access and modify the data in a database from an application.

The aim of this module is to instil students with an appreciation of the different ways that data can be stored. By introducing multiple approaches (e.g. SQL-based and NoSQL) students will learn how to select the most appropriate storage for a given application, taking into account the complexities around accessing and writing data. Students will also learn how to construct software to connect an application to a database securely.

The module will also cover concurrency control, backup and recovery, user management, and web development with databases, The module will also introduce modern concepts concerning data systems, which might include big data, the cloud, data warehousing, blockchain, decentralised systems, and governance.

View an example full module specification

COMM514: Research Project

In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results.

Research topics can be selected from across the breadth of computer science, data science and related topics. The project may include theoretical analysis, as well as practical software implementation.

This module aims to give you in-depth experience of research in an area relating to your programme of study. It will help you prepare for projects both in an industry or commercial setting, as well as in further postgraduate research work, such as a PhD. The module builds on the knowledge and skills you have acquired in the taught modules of the programme to allow you to investigate an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis and presentation of results.

View an example full module specification

ECMM443: Introduction to Data Science

In this module, you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling and visualisation, statistical modelling, network and text data analysis. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises and individual study will consolidate your learning and provide the foundations for later study.

This module will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will evolve to reflect current issues in data science, providing students with the tools to formulate data science problems and construct pipelines to begin to solve them technically.

Lectures will be accompanied by data analysis exercises. A series of guided practical exercises will develop skills in programming (in Python), data handling and visualisation.

View an example full module specification

ECMM462: Fundamentals of Security

Our modern life depends on the security of computerised systems ranging from social aspects (e.g. phishing) to technical and mathematical aspects (e.g. access control, encryption). In this module, you will learn the fundamental concepts required for starting a career in various areas related to security (e.g. cyber security, data security, information security, computer security). You will learn core security concepts (e.g. authenticity, confidentiality, anonymity, privacy) and core technologies (e.g. encryption, authentication, authorisation). Moreover, you will learn the basic attacks on security systems and approaches for reasoning about the correctness of security techniques.

The aim of this module to create awareness of the need for security and privacy in modern life, and to introduce the fundamental security and privacy mechanism used in modern computer systems. We will explore topics such as fundamentals of computer security, technology and principles of network security, cryptography, authentication and digital signatures, access control mechanisms, privacy, and anonymisation.

In more detail, the aims of the module are to give you an understanding of:

View an example full module specification

ECMM463: Building Secure and Trustworthy Systems

Building secure and trustworthy systems, i.e. systems that are hard to attack and protect the privacy of their users, are extremely hard to build. In this module, you will learn the foundations of building secure (software) systems ‘right from the beginning’. You will learn how to assess the threats of a system that need to be mitigated while building it, the risk assessment of vulnerabilities, as well as various approaches (e.g., defensive programming) and techniques for building secure systems. The module focuses on defensive security techniques that might be used by “blue teams.”

This module aims to give you a broad understanding of techniques for assessing the risks a modern IT system is exposed to. Driven by these risks, we will discuss several defensive security techniques for building security and trustworthy (software) systems. In more detail, the aims of the module are to enable you to assess the security of software architectures understand the principles of secure software architectures understand software vulnerabilities, their causes, and impact to develop secure software using defensive programming techniques to understand the principles of security testing and verification techniques.

View an example full module specification

ECMM464: Security Assessment and Validation

Even if systems have been developed with security in mind, their security needs to be assessed regularly, as, e.g., new attacks might be developed. Thus, assessing and validating the security of systems, e.g., penetration testing is an important part of cyber security. In this module you will learn the theory and practice of assessing the security of systems and applications both using manual techniques as well as automated approaches. The module focuses on offensive security that might be used by “red teams.”

This module aims to give you a broad understanding in analysing the weaknesses of a system, i.e., the areas an attacker would most likely attack a system. Driven by the discovered weaknesses, we will discuss several offensive security techniques, I.e., simulate how a threat actor (attacker) might gain access to a system or the data processed by a system. In more detail, the aims of the module are to enable you to assess the security weaknesses of a system develop a strategy how to attack a system understand the both the social and technical foundations for attacking systems or organisations understand the ethical responsibilities of an offensive security researcher.

View an example full module specification

LAWM155: Human Rights and Digital Technologies

Digital technologies raise many legal issues for the protection of human rights. In this module you will learn how international human rights law responds to these legal issues. You will acquire and deepen your understanding of the rights to privacy, data protection, freedom of expression, non-discrimination and due process in the digital age. You will have the opportunity to critically think about how the law should evolve to better regulate these technologies. Knowledge of this area of law is a strong and desirable asset for students wishing to pursue a career in the private and public sectors alike.

View an example full module specification

LAWM163: The International Law of Cyber Operations

Cyberspace is surprisingly difficult to define, but there is no question that it has become a venue of strategic competition and contestation among States and other stakeholders. Incidents such as Stuxnet, WannaCry, or NotPetya have been dominating the headlines, raising the questions of attribution and accountability for the havoc they have wreaked around the world. Meanwhile, over 100 States have been developing military cyber capabilities, many of which have already been put to use in wartime including in the ongoing armed conflict between Russia and Ukraine.

Against this fast-evolving and complex backdrop, this module is designed to equip you with a deep understanding of how international law governs - and thus restricts - cyber operations both in peacetime and during armed conflict. We will explore how existing international legal concepts such as sovereignty, non-intervention, or State responsibility apply in the cyber context and what practical consequences this entails. We will also delve into the application of international humanitarian law to cyber operations during armed conflicts, with a particular focus on the question of protection of civilian data, the trend of civilianization of military cyber activities, and the regulation of information operations.

By completing this module, you will position yourself as a desirable candidate for professional opportunities in the fields of international law, diplomacy, and cybersecurity.

View an example full module specification

SOCM033: Data Governance and Ethics

Data science, machine learning, artificial intelligence and 'big data' have become central to every aspect of social life. How can these complex and powerful technologies best be managed and governed for the benefit of society now and in the future? In this module you will: (1) identify some of the main risks and ethical/legal challenges involved in the widespread automation and digitalisation of services characterising 21st century life (for example, the clash between individual desire for privacy, frameworks for data ownership and the institutional commodification of personal data); (2) examine whether and how such concerns can be handled; and (3) discuss the responsibilities of data scientists and other producers of technologies for data analysis towards their proper use.

View an example full module specification

ECMM422: Machine Learning

Machine learning has emerged mainly from computer science and artificial intelligence, and draws on methods from a variety of related subjects including statistics, applied mathematics and more specialized fields, such as pattern recognition and neural computation. Applications are, for example, image and speech analysis, medical imaging, bioinformatics and exploratory data analysis in natural science and engineering. This module will provide you with a thorough grounding in the theory and application of machine learning, pattern recognition, classification, categorisation, and concept acquisition. Hence, it is particularly suitable for Computer Science, Mathematics and Engineering students and any students with some experience in probability and programming.

In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis.

View an example full module specification

ECMM423: Evolutionary Computation and Optimisation

Evolutionary computation is the study of computational systems that use ideas and derive their inspiration from natural evolution. Its techniques can be applied to optimisation, learning and design. Building on the foundations of evolutionary algorithms, this module develops an understanding of research trends in evolutionary computation and in particular advanced algorithm formulations to address more complex optimisation problems. Example topics covered in this module include algorithms designed to address many-objective, noisy and dynamic optimisation problems, and advanced methods including hyper-heuristics, human-in-the-loop and surrogate assisted optimisers. This is a research-led module appropriate for students with an interest and a background in bio-inspired problem-solving techniques and optimisation who have adequate programming and mathematical experience.

The aims of this module are to:

• Introduce advanced concepts and techniques in the field of evolutionary computation and their application to complex optimisation problems
• Provide students with experience of presenting complex topics to their peers and to participate in Q&A sessions similar to those experience in a conference setting

View an example full module specification

ECMM445: Learning from Data

Artificially intelligent machines and software must assimilate data from their environment and make decisions based upon it. Likewise, we live in a data-rich society and must be able to make sense of complex datasets. This module will introduce you to machine learning methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.

The module will also cover some recent neural network architectures and related learning algorithms.

This module aims to equip you with the fundamentals of machine learning and at the same time discuss technical aspects of some well-known machine learning models and related learning algorithms. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods (clustering and dimension reduction). The module will cover kernel methods and neural networks (feed-forward architectures only).

View an example full module specification

ECMM450: Stochastic Processes

A stochastic process is one that involves random variables. A large number of practical systems within industry, commerce, finance, biology, nuclear physics and epidemiology can be described as stochastic and analysed using the techniques developed in this module. The systems considered may exist in any one of a finite, or possibly countably infinite, number of states. The state of a system may be examined continuously through time or at fixed and regular intervals of time.

You will study processes whose changes of state through time are governed by probabilistic laws, and you will learn how models of such processes can be applied in practice.

Pre-Requisite Modules:

View an example full module specification

ECMM461: High-Performance Computing

The demand for ever-increasing computational power drives the development and exploitation of high-performance computing that underpins leading edge research in computationally intensive engineering technologies fields. This module is designed to equip you with a solid foundation and useful skills in high-performance and distributed computing. In this module, you will learn about current high-performance computer architectures and how the computer architecture influences the performance of algorithms and programs. You will also develop skills in parallel algorithm design and parallel programming, and will gain experience of using a high-performance computing system.

This module aims to provide you with a thorough grounding in parallel programming and the architectures used in high-performance computing. After presenting the fundamental ideas and basic concepts of high-performance computing, the module outlines the architectures, components and parallel programming of high-performance computers. The module will introduce you to recent developments and future trends in architecture and algorithms in high-performance computing.

View an example full module specification

MTHM508: Bayesian Philosophy and Methods in Data Science

Since the 1980s, computational advances and novel algorithms have seen Bayesian methods explode in popularity, today underpinning modern techniques in data science and machine learning with applications across science, social science, the humanities and finance.

This module will cover the Bayesian approach to modelling, data analysis and statistical inference. The module describes the underpinning philosophies behind the Bayesian approach, looking at subjective probability theory, the notion and handling of prior knowledge, posterior inference, and how this approach differs to classical approaches to statistics. It will explore simulation-based inference in Bayesian analyses and develop important algorithms for Bayesian simulation by Markov Chain Monte Carlo (MCMC) such as the Gibbs sampler and the Metropolis-Hastings algorithm. Finally, we’ll apply the techniques and tools developed through the module to fit a wide range of models using modern Bayesian inference software, enabling you to apply techniques discussed in the course to a variety of real datasets, ensuring the student is equipped to use Bayesian methods in future jobs.

This module is appropriate for MSc students who have not completed a mathematics undergraduate degree, with the focus of the assessment on the understanding and application of techniques. Experience of a programming language such as R or Python, and some basic statistics/probability, is beneficial but not required.

View an example full module specification