Networks and Security - MSc

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.

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• Nigel P. Smart. "Cryptography Made Simple", 2016, Springer. Available at: https://link-springer-com.chain.kent.ac.uk/content/pdf/10.1007%2F978-3-319-21936-3.pdf

• Jonathan Katz and Yehuda Lindell. "Introduction to Modern Cryptography", 2nd edition, 2015, CRC Press.

• Alfred J. Menezes, Paul C. van Oorschot and Scott A. Vanstone. Handbook of Applied Cryptography, 1997, CRC Press. Available at: http://cacr.uwaterloo.ca/hac/

• Charles P. Pfleeger and Shari Lawrence Pfleeger, "Security in Computing", 5th ed., 2015,

Prentice Hall

• Dieter Gollmann, "Computer Security", 3rd Edition. 2011. John Wiley and Sons.

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The crowning piece of most Masters degrees is the Masters Project in which you apply a wide range of skills learned in the taught modules to an interesting research problem or practical application of your choice. The Project Research module provides useful transferable skills for doing the project, and supports you in some preparatory tasks such as literature study and project planning.

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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.

• Network forensics and incident response.

• Emerging network protocols

• IPv6 security.

• Honeypots and honeynets.

• Software-defined networking.

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This module provides an introduction to object-oriented programming using the popular Java language. It is designed for beginners who have not studied computer programming before. By the end students will be able to develop simple programmes using Java.

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Building upon Introduction to Object-Oriented Programming, this module covers the design and implementation of high-quality software using OO techniques. Systems are modelled as configurations of objects communicating with one another. Techniques (e.g. inheritance) are introduced which allow objects to play different roles within a system. These two concepts are key to the support for adaptation and reuse that OOP provides. Much emphasis will be placed on gaining a deep understanding of these concepts and applying them in practice by developing programs in Java. The remainder of the module will explore software component frameworks, specifically those that come packaged with Java, placing most emphasis on the frameworks to support the structuring and manipulation of data (data structures and algorithms).

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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.

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Students undertake several projects for the Kent IT Consultancy (KITC). Each of these will be either a commercial project for an external client, or an internal development project, e.g. developing a future service offering for the KITC.

In addition to project work, students will be expected to engage in ongoing tasks related to the operation of the consultancy, including marketing, sales and mentoring/buddying colleagues.

Each assignment will be carried out under the supervision of KITC management and in accordance with client requirements, with deliverables de?ned by negotiation with the client.

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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 preceded by an exploratory stage in which students review and summarise relevant literature or other technical background, including in a verbal presentation, and gain specific skills relevant to their project. 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.

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Introduction to software development environments and the facilities they provide. Development of simple applications in these environments, using a broad range of the facilities provided. Software libraries and frameworks, and their use in developing and testing software systems. Use of development frameworks' facilities for project and source-code management, automated testing, refactoring and profiling. Deploying applications across multiple platforms using installers and build-systems, continuous integration and deployment.

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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.

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Fundamentals of Image Processing

General introduction to digital image processing; image acquisition, quantisation and representation; Affine transforms; image enhancement techniques: contrast manipulation, binarisation, noise removal (spatial and frequency domain); edge detection techniques; image segmentation: edge-based, region- based, watershed; Hough transform; image feature extraction; advanced image processing: morphological operations, colour image processing, various image transforms (Fourier, wavelet, etc).

Fundamentals of Pattern Recognition

Patterns and pattern classification, and the role of classification in a variety of application scenarios, including security and biometrics. Basic concepts: pattern descriptors, pattern classes; invariance and normalisation. Feature-based analysis. Texture analysis. The classification problem and formal approaches. Basic decision theory and the Bayesian classifier. Cost and risk and their relationship; rejection margin and error-rate trade-off. Canonical forms of classifier description. Estimation of class- conditional distributions; bivariate and multivariate analysis. Euclidean and Mahalanobis distance metrics and minimum distance classifiers. Parametric and non-parametric classification strategies. Linear discriminant analysis. Clustering approaches, and relationship between classifier realisations. Practical case studies. Introduction to non-classical techniques such as neural network classification.

Security Applications and Image Analysis

Signature authentication and analysis, Digital watermarking, Content hidden in Images and Video, Steganography. Image forensics.

Implementation Essentials

Programming and data analysis using MatLab and other software tools as appropriate. Introduction to practical work using MatLab. Students not familiar with Matlab programming will be provided with appropriate introductory material before this lecture.

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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.

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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.

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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.

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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.

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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.

Technical subjects include a description of the various security models, and showing how authorisation policies can be automatically enforced. The legal and data protection issues associated with information management are also addressed, as are the usability issues of security technologies.

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Neural networks will be placed into a historical perspective related to neuro-biology and in the context of the artificial intelligence hypothesis. Students will familiarise themselves with the Leabra/Emergent environment.

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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. It is therefore proposed to allow students the opportunity to become exposed to these types of methods for use in their late careers.

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