Networks and Security - MSc

The module will cover a mixture of theoretical and practical topics in the area of mobile devices and the Internet of Things (IoT), that is, the use of Internet technologies to access and interact with objects in the physical world. This will include coverage of the range of sensor and actuator devices available, ways in which they communicate and compute, methods for getting information to and from IoT-enabled devices, ways of visualising and processing data gained from the IoT, and associated privacy and security issues. Application development for mobile devices such as smartphones will also be introduced using a popular mobile platform.

This module provides for well-qualified computer science students entering the MSc programme from a range of backgrounds. These students will have good programming skills but will not necessarily have used Java or another object-oriented language extensively. This module seeks to ensure that students have the Java and object-oriented design skills necessary for the rest of their programme.

Introduction, including a review of network techniques, switching and multiple access. High speed local area networks. Network protocols, including data link, network, transport and application layers and their security issues. Problems of network security and mechanisms used to provide security such as firewalls and network security protocols. Real time data transmission and quality of service. Naming and addressing and related security concerns. Security of IEEE 802.11 networks.

This module starts with the fundamental mathematical concepts to build cryptographic primitives. A key objective is to learn to implement the primitives without using programming libraries, learn the formal security notions and models for the primitives, and the use of the primitives in practical applications like blockchains.

The second part of the module covers the key application areas of authentication, authorisation and accountability (AAA). Included here are foundational topics of user and non-user authentication (including issues with password and biometric authentications), as well as access control and authorisation, along with matters related to accountability.

The project consists primarily of an extended period during which students undertake a substantial piece of work and a report on this in the form of a dissertation. It is usually preceded by an exploratory stage in which students review and summarise relevant literature or other technical background, and gain specific skills relevant to their project via a series of taught workshops. It may be permitted to undertake the work in groups, particularly for projects with a development focus. However, the dissertations are produced individually. The project examines the student's ability to research technical background, to understand and expand on a specific problem commensurate with their programme of study and relate it to other work, to carry out investigations and development (as appropriate), to describe results and draw conclusions from them, and to write a coherent and well organised dissertation demonstrating the student's individual reflection and achieved learning.

A synopsis of the curriculum:

• Network security and cybercrime.
• Analysis of real world network security incident (IoT botnet).
• Email security issues (spam and phishing attacks; spam filtering systems).
• Spyware (system vulnerabilities; stealth techniques; detection and removal).
• Network-related data security (data breaches; data loss prevention; remote sniffer detection).
• Security of WiFi networks.
• IoT network security
• Network forensics and incident response.
• Emerging network protocols
• IPv6 security.
• Honeypots and honeynets.
• Software-defined networking.

This module introduces students to the functional programming paradigm, using at least one modern functional programming language to put the core concepts into practice. The module will develop both the foundation and theory of this paradigm, as well as the practice and application of the paradigm to solve problems and build systems. The module will core topics, including:

• Functions as first-class language constructs and as a central organising principle;
• Higher-order functions and compositional programming;
• Basic semantics of functional languages;
• The role of types in programming;
• Algebraic data types and pattern matching;
• Recursion and recursive data types;
• Differences with imperative and object-oriented programming paradigms;
• Properties of programs, (e.g., purity, side-effect freedom, totality, and partiality).
• The lambda-calculus as a programming model and foundation.
• BNF grammars for representing context-free syntax, and its relation to ADTs and language manipulation.
• Testing and issues of building correct software.

The module will develop practical skills in programming and problem solving using functional programming. There will also be a chance to apply functional programming to help understand better concepts in logic and mathematics.

Later parts of the module will then consider concurrent programming in the context of functional programming, including concurrent programming models and primitives (e.g., message-passing concurrency), parallelism, synchronisation and communication, and properties of deadlock, communication-safety, and starvation.

The module focuses on teaching the foundations of language-based security including but not limited to the use of formal logics, type systems, process calculi and proof carrying code for reasoning about the security properties of programs.

An overview of basic concepts related to eHealth and a perspective on current HIT (Health Information Technology) and innovation. Review of current healthcare related IT systems. The use of information technology for handling clinical data, health systems. Data representation and knowledge management. Security and privacy. Ethics and legal requirements of eHealth systems. Clinical decision support systems. TeleHealth tools for remote diagnosis, monitoring, and disease management. Delivery and monitoring platforms for both hospitals and home environment. Innovation in eHealth systems leading to start-up companies.

This module will give students an overarching introduction to quantum information processing (QIP). At the end of the course the students will have a basic understanding of quantum computation, quantum communication, and quantum cryptography; as well as the implications to other fields such as computation, physics, and cybersecurity.

We will take a multi-disciplinary approach that will encourage and require students to engage in topics outside of their core discipline. The module will cover the most essential mathematical background required to understand QIP. This includes: linear algebra, basic elements of quantum theory (quantum states, evolution of closed quantum systems, Born's rule), and basic theory of computing. The module will introduce students to the following theoretical topics: quantum algorithms, quantum cryptography, quantum communication & information. The module will also address experimental quantum computation & cryptography.

This module will aim to familiarise students with core concepts (e.g. Locard's exchange principle, and legal admissibility of digital evidence) and best practices (e.g. the ACPO Good Practice Guide for Digital Evidence, Contemporaneous Notes taking, and the SWGDE guidelines) underpinning digital forensic investigations. It introduces methodologies that guide the digital investigative process (i.e., collection, interpretation, analysis and reporting), and key techniques that can be applied for interpretation and analysis of digital evidence in the context of digital forensics in general (e.g., hashing, and file carving), computer forensics (e.g., windows registry analysis and metadata analysis), and multimedia forensics (e.g., multimedia source analysis for device identification, and multimedia content analysis for forgery detection).

The module also discusses challenges faced by digital forensics due to the increasing volume and diversity of data sources involved in investigations.

This module aims to familiarise students with general concepts about privacy, privacy issues in selected application contexts of privacy enhancing technologies (e.g., Internet and web, mobile computing, online social networks, IoT), selected privacy enhancing technologies including data anonymisation (e.g., k-anonymity and differential privacy), anonymous communication (e.g., Tor), web and mobile privacy tools, and socio-technical related topics aspects of privacy (e.g., privacy behaviours, privacy policies, usability, and relevant legal issues).

This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. Students will learn about the philosophy of AI, how knowledge is represented and algorithms to search state spaces. The module also provides an introduction to both machine learning and biologically inspired computation.

This module covers the design and implementation of high-quality software, and provides an introduction to software development for Artificial Intelligence (AI). In this module, students will gain an understanding of data analysis and statistics techniques, including effective graphical representations.

Throughout the module, students will learn to embed data analysis and statistics concepts into a programming language which offers good support for AI (e.g., Python). Students will learn to use important AI-purposed libraries and tools, and apply these techniques to data loading, processing, manipulation and visualisation.

This module explores a range of different data mining and knowledge discovery techniques and algorithms. You learn about the strengths and weaknesses of different techniques and how to choose the most appropriate for any particular task. You use a data mining tool, and learn to evaluate the quality of discovered knowledge.

This module investigates the whole process of information security management and associated activities including the concepts used and practices prescribed by relevant standards, such as those defined by ISO/IEC. A holistic view of information security management is taken, including risk management, the formulation of security policies, business continuity and resilience. Selected socio-technical topics that are important for information security management will also be covered. These shall include AAA (authentication, authorisation and accountability), important legal aspects especially data protection and privacy laws, data protection impact assessment, usability analysis and management, wider human factors in cyber security such as social engineering attacks and the importance of a positive cyber security culture for encouraging secure behaviours of employees and users.

In this module you learn what is meant by neural networks and how to explain the mathematical equations that underlie them. You also familiarise yourself with cognitive neural networks using state of the art simulation technology and apply these networks to the solution of problems. In addition, the module discusses examples of computation applied to neurobiology and cognitive psychology. The module also introduces artificial neural networks from the machine learning perspective. You will study the existing machine learning implementations of neural networks, and you will also engage in implementation of algorithms and procedures relevant to neural networks.

There is an increasing use of nature-inspired computational techniques in computer science. These include the use of biology as a source of inspiration for solving computational problems, such as developments in evolutionary algorithms and swarm intelligence. Similarly, there is now also an increasing interest in understanding how biological, chemical and other natural systems compute, and how this could be exploited for practical applications. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their later careers.

The module will explore existing and emerging legal issues in cyber security, cybercrime, privacy and data protection, including the domestic and cross-boundary legal regulatory frames and their associated ethical dimensions. Topics covered include cybercrime, privacy and data protection, Internet and cyber surveillance, cross-border information flows, and legal structures. Students will be challenged to critically examine the ethics and management of cyber data. It will require students to assess emerging legal, regulatory, privacy and data protection issues raised by access to personal information.

Data types: nominal, numerical, ordinal, text, audio, visual, temporal and non-temporal. Basic descriptive statistics: measures of average and spread, different ways of graphing data. Choosing appropriate and valid methods for the analysis and presentation of data, and understanding the limitations of methods. Data at different scales, including big data, and the computational challenges of processing data at scale. The process of discovering useful knowledge from data: including understanding the need for preprocessing and cleaning data, the challenges of gathering relevant data, and the need to present results in a comprehensible and actionable way. Data mining: classification/regression and clustering, and the idea of predictive analytics. Elements of information retrieval from text. Vector representations of text documents. Fairness and ethical issues concerning data.

This module looks into the training of modern deep neural networks: backpropagation, regularisation, automatic differentiation, computational graphs. Introduces different types of deep neural networks, such as, LSTM, convolutional networks, and autoencoders. Presents the theoretical underpinnings of deep learning and its mechanisms. Delves into selected recent advanced topics in deep learning. Examines applications of deep learning.

The module looks at a number of advanced topics in cyber security that are important for understanding, finding, researching and assessing security solutions. Example topics include:

• Digital steganography and watermarking, and its increasing role in modern malware;
• CAPTCHAs and other mechanisms to distinguish bots from humans remotely;
• AI in security, for example, the role of deep learning and adversarial examples in cyber security;
• Security in AI, for example, the protection of machine learning techniques against cyber threats;
• Random number generators and their relevance in password and nonce generation;
• Advanced malware threats such as ransomware, covering their evolution and providing some insights into likely future trends, including economic aspects.
• Advanced topics in research related to human factors and usable security, e.g., user behaviour and their relationship to cybercrime, positive security, user profiling and
• modelling;
• Quantum cyber security and the development of quantum-resistant cyber security systems based on quantum mechanics;
• Advanced topics in IoT security, covering new developments and trends, threats and mitigations.

The focus of the module is on the development of the advanced English language competence necessary for post graduate study in scientific disciplines. This includes the ability to interpret and evaluate authentic scientific texts; analyse, discuss and summarise written and visual information both in writing and orally; organise written texts effectively and submit them in grammatically accurate English, and present the results of research orally in a coherent and stimulating way.