CryptographyMSc

Applications in Cryptography and Cryptanalysis

This module aims to introduce you to applications of cryptography in the 21st century connected, digital world. Its content and delivery are linked to CS7064 Information Security you will be studying at the same time or will have previously studied. This companion module, taken by students on other pathways, contains a section of c4 weeks that describes key aspects of modern cryptography (focussed on asymmetric/public key algorithms). In these weeks you will encounter key concepts and definitions and the broad structure of algorithms in CS7064 but will study them in more depth and with a stronger mathematical underpinning in the corresponding sessions that form part of this module. This approach will allow you to consolidate your knowledge across two sessions in a single week for key topics of the syllabus allowing sufficient time for you to explore techniques in a lab session using MAPLE software.

Beyond these 4 weeks there are three other principal themes for this module:

· To see how cryptanalysis works in historic and modern ciphers; this is illustrated by seeing ways that historic ciphers were broken and a detailed analysis of attacks on RSA that need to resisted in a successful implementation

· To give you a historical perspective on the development of cryptography and the way the subject has evolved; this theme will be developed by visits including to the Museum of Cryptography at Bletchley Park.

· To enable you to appreciate the changing landscape that cryptography inhabits and how developing technologies such as quantum computing will influence its future development. The module concludes with a survey of newly established standards for quantum resistant algorithms based around lattices and error correcting codes.

Additionally, the module aims to provide you with the background and familiarity with key concepts to enable you to make an informed selection of a topic for your major project/dissertation. This will necessarily include the identification of key ethical and social implications in the landscape that cryptography inhabits.

Discrete Mathematical Structures

This module, together with MA7010, Number Theory for Cryptography provides you with the essential grounding needed to complement the algorithms and techniques encountered in spring semester modules in cryptography and information security. It aims to demonstrate the theoretical underpinning that delivers security, particularly of asymmetric public key algorithms while also allowing you to appreciate the limitations and potential vulnerabilities of systems encountered elsewhere in their course. It is designed to complement, in particular, MA7011 -Applications in Cryptography and Cryptanalysis and can be taken either before or after this module depending on your start point.

You will explore techniques in discrete mathematics that have application in cryptography in a thorough way with emphasis on getting to a level where the mathematics of cryptography and the language used is accessible irrespective of your background and prior study. Beginning with a brief review of topics normally encountered in undergraduate mathematics and computer science courses, new topics will then be introduced from first principles with an emphasis on supported learning via problem classes, worked examples and formative assessment.

Information Security

The module is concerned with the study and application of tools and techniques that enable the protection of information and other resources of enterprise information systems. Increases in storage, manipulation, and transfer of data across computer networks requires effective encryption techniques. This module will provide insight into some of those techniques, algorithms and their development through history. Part of the course is dedicated to the mathematics (number theory, finite fields and elliptic curves) relevant to cryptography with techniques developed using software such as Maple. The focus will also be on the analysis, design and implementation of tools and techniques that achieve the three goals of confidentiality, integrity and authenticity in security computing. Particular focus will be on the management framework that facilitate the accomplishment of the above three goals. Importantly the module will address the ethical framework of information security, the issues around privacy and data protection and the rights of private citizens to access strong encryption. Throughout the module connections with other aspects of artificial intelligence and cybersecurity will be emphasised through the examples and case studies chosen.

MSc Project

The module provides students with the experience of planning and bringing to fruition a major piece of individual work. Also, the module aims to encourage and reward individual inventiveness and application of effort through working on research or company/local government projects. The project is an exercise that may take a variety of forms depending on the nature of the project and the subject area. Students will be encouraged to carry out their projects for local companies or government departments.

The objectives of the module are:

· To develop the ability to produce detailed specification relevant to the problem of investigation.

· To manage the project by confining the problem within the constraints of time and available resources.

· To effectively research the background material on the topic using a variety of sources and to develop ability to conduct critical analysis and draw conclusions.

· To apply and/or extend the knowledge acquired in the taught core modules to a new area of application or investigation.

· To use relevant tools and techniques for designing, testing, analysing, and critical evaluation.

· To demonstrate the originality in the application of new knowledge and skills.

· To effectively communicate the work to others by verbal and documentation media.

· To raise awareness in potential business development opportunities in an area pertinent to the topic.

Number Theory for Cryptography

This module, together with MA7009, Discrete Mathematical Structures provides the essential grounding you will need to complement the algorithms and techniques encountered in spring semester modules in cryptography and information security. It aims to describe relevant topics in the theory of the natural numbers that underpin much of 21st century cryptographic security. It is designed to complement, in particular, MA7011 - Applications in Cryptography and Cryptanalysis; you can take that either before or after this module depending on your start point without disadvantage.

Additionally, this module will support a series of mini investigations designed to develop your ability to take the ideas you encounter, apply them with realistic datasets and draw appropriate conclusions. These will principally be delivered via procedures written in the mathematical programming language MAPLE or the Python programming language in which you will be supported as you gain fluency.

The module will principally introduce you to topics in number theory but their application to cryptography will be stressed as they are introduced and in the initial weeks the module will also provide a brief overview of the subject of cryptography and its historical context.

Advanced AI Technologies

Artificial Intelligence research and development spans over a period of nearly 70 years. During this period the academicians have been trying to address most of the aspects of human intelligence known from other fields – mathematics, philosophy, psychology, linguistics, biology, etc. As a result, AI was clearly partitioned into several areas, each with its own methodology of investigation and technology of problem solving – state-space problem solving, decision making, automated reasoning, knowledge-based planning and machine learning. While during different periods one or another were attracting the attention, all of them found practical applications and gradually evolved, reaching bigger depth and maturity.

This module covers the evolution of AI and provides a thematic coverage of the various contemporary branches of the discipline. AI paradigms, models, and technologies are covered and students are given the opportunity to investigate and apply those that interest them most.

Cybercrime and Cyber Security

This module provides a broad introduction to cybercrime and cyber security evolution. The module examines the relationship between advances in Internet-based and digital technologies, and their criminal exploitation within cyberspace. It examines a wide range of cyber threats, attacks and risks, and the strategies employed to mitigate these, including the laws that are in place to protect and prevent online crimes/cybercrimes.

The module provides essential coverage of the principles and concepts underpinning cybercrime and cyber security, maintaining focus on the identification, examinations and assessment of the key threats, attacks and risks, and in areas related to legal, ethical, social and professional issues.

Financial Mathematics

This module provides an introduction to some of the key mathematical methods used in financial calculations and how they are applied to the valuation of projects in the presence of uncertainty. There will be a particular focus on Discounted Cash Flow and Real Options methods but also on recent developments in the field of project valuation.

Methods such as Monte-Carlo simulation for financial options valuation and the Capital Asset Pricing Model (CAPM) with the aim of optimising a portfolio will also be explored using real financial data.

The module aims to:

1. Provide students with a set of up-to-date mathematical tools for project valuation with a particular focus on financial applications.

2. Provide a foundation in modern developments in optimisation theory and methods and introduce essential topics of unconstrained and constrained optimisation.

3. Explore the applications of Capital Asset Pricing models to problems involving decision making in modern portfolio management using real world financial data

Machine Learning

This module provides a comprehensive overview on the use of data and algorithms to imitate how human learn as a branch of Artificial Intelligence (AI). It also provides practical skills using a programming language such as python for working with various tools to build machine learning solutions. The knowledge and skills obtained can be used in many tasks where extracting knowledge and gaining insight from data is of crucial importance for the competitiveness and the effectiveness of the businesses – customer profiling, product recommendations, market trends analysis, cybersecurity, investment monitoring, stock price prediction, etc. Some basic programming skills using languages such as Python or other relevant languages is required.

Statistical Modelling and Forecasting

This module will introduce students to modern statistical modelling techniques and how those techniques can be used for prediction and forecasting. Throughout the statistical environment and software R will be used in conjunction with relevant statistical libraries.
The module will, introduce modern regression techniques (including smoothing), discuss different model selection techniques (including the classical statistical hypothesis) and how those techniques can be used for prediction purpose.

Prior learning: Statistical knowledge desirable.

The module aims to:

1. Equip graduate students with modern statistical techniques
2. Provide students with some selected advanced statistics topics including forecasting
3. Prepare students to be able to read and understand professional articles
4. Prepare students to carry on their own research and use modern statistical techniques as one of the tools for their research.