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.

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. Real time data transmission and quality of service. Naming and addressing. Security of IEEE 802.11 networks. Recent developments: topics will change from year to year

• A general introduction to networks and networking protocols, especially TCP/IP.

• Overview of important Internet application protocols: HTTP, SMTP, DNS, LDAP.

• A study of cryptographic algorithms including symmetric and asymmetric techniques and the distinction between encryption and signatures.

• Security mechanisms used with operating systems, including: usernames/passwords, access control lists and capabilities.

• Problems of network security including wiretap, replay, masquerade and denial of service. Mechanisms to provide security such as firewalls and VPNs.

• Viruses and worms.

• Distributed Mechanisms, including client authentication (Needham-Schroeder, Kerberos and others); public key infrastructures and certification, with treatment of chains and authority, and the problem of revocation.

• Securing email systems: PGP and S/MIME

• Identity management systems: e.g. Shibboleth, Passport, CardSpace, OpenID.

• Basic introduction to information risk management and information security management.

• Security of IEEE 802.11 networks (aka Wi-Fi), presentation and discussion of their security protocols: WEP, WPA, WPA2, IEEE 802.11i and RSN.

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.

Email security issues: spam and phishing attacks; spam filtering systems. Spyware: system vulnerabilities; stealth techniques; detection and removal. Web based user tracking and adware. Network security and cybercrime. Data breaches and data loss prevention. Network forensics, network monitoring and packet analysis. Security of WiFi networks.

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.

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).

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.

Cloud computing describes a new supplement, consumption, and delivery model for IT services based on the Internet, and it typically involves over-the-Internet provision of dynamically scalable and often virtualized resources. It is a by-product and consequence of the ease-of-access to remote computing sites provided by the Internet. This frequently takes the form of web-based tools or applications that users can access and use through a web browser as if it were a program installed locally on their own computer.

This module builds on CO871 Advanced Java for Programmers and extends your knowledge to cover the language C++ which is widely used by professional programmers.

Concurrent design and programming skills are of growing importance as multi-core processor technology advances. A sound understanding of fundamental concurrency concepts and obstacles is essential. This module introduces fundamental theories of concurrency. It discusses how designs can be made parallel and identifies the common faults in concurrent programs and how to avoid them. It introduces a range of widely used programming paradigms and techniques for writing concurrent programmes.

The amount of data generated worldwide is more than doubling every year. Traditional data analysis techniques are inadequate for dealing with the vast ocean of data. This module introduces modern techniques, platforms and tools for analysing large data sets efficiently, along with key applications, to equip students to join the new generation of data scientists sought after by industry and academia.

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.

This module covers the basic principles of machine learning and the kinds of problems that can be solved by such techniques. You 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 will introduce students to fundamental concepts of functional and concurrent programming, using a suitable language (e.g. Erlang) as a vehicle to put these concepts into practice. The first part of the module will cover basic ideas in functional programming, such as expressions, types, values, lists, pattern-matching and recursion, together with the specific language concurrency model, including process creation, message sending and receiving. Good concurrent design practices will be considered, based on networks of communicating processes (e.g. Actor and CSP models), and avoiding problems such as deadlock, livelock and starvation.

The later part of the module will cover more advanced topics (higher-order functions) and look at alternative concurrency models (e.g. synchronous, channel-based, join-based and shared-memory) and their relationship to the model described in the first part of the module. Alongside this, consideration will be given to the relevance and applicability of functional and concurrent programming for use in real applications.

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 security management. A holistic view of security management is taken, starting with risk management and the formulation of security policies. Technical subjects include a description of the various security models, and showing how authorisation policies can be automatically enforced. The legal and privacy issues associated with information management are also addressed, as are the usability issues of security technologies. Finally, the module concludes by investigating how security has already been inbuilt into some existing applications, and how security issues will effect the uptake of ubiquitous computing systems

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.

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.

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.

The module looks at federated identity management, privacy protection, viruses and worms, hacking, secure architectures, formal verification methods, e-mail security, secure software development methods and tools.

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.

In this module you will develop the advanced English language skills needed for post graduate studies in Science. This includes the ability to interpret and evaluate authentic scientific texts; analyse, discuss, summarise and synthesise 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.

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.

The project consists of an extended period during which students work on a specific piece of project work and a report on this work in the form of a dissertation. Project work, particularly with a development focus, may be undertaken in groups. However, the dissertations are produced individually. The project examines the student's ability to research the literature, 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, and 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.