Cyber Security and Big Data MSc

What you'll study

You will learn from the most influential thought leaders, pioneering researchers and creative innovators, exposing you to the latest theories and developments from across your discipline.

Modules

Our Cyber Security and Big Data MSc covers a wide range of topics; to give you a taster we have expanded on some of the modules affiliated with this programme and the specific assessment methods associated with each module.

• Compulsory
• Optional

Applied Cryptography

Applied Cryptography

This module will cover modern cryptographic algorithms and mechanisms for cyber security with emphasis on the applications and engineering implementations.

The first part covers some theoretical foundations of cryptography, cryptographic building blocks as well as the basic, intermediate and advanced protocols. The second part is about cryptographic techniques including key and its management, algorithm types and modes. The third part covers cryptographic algorithms which are widely used in the network and security industry, including various ciphers such as block ciphers (DES, AES, RC2, Blowfish, etc.) and stream ciphers (A5, RC4, SEAL, and cascading multiple stream ciphers), one-way hash functions, (MD2, MD5, SHA), public-key algorithms (RSA, ElGamal, Elliptic Curve), digital signature and key exchange algorithms.

The fourth part covers the applications and implementations of selected algorithms and protocols to address security issues in data and security service industry in the real world.

The aim of this module are to introduce the basic concepts of cryptography and develop students' knowledge in cryptographic protocols, techniques, algorithms and implementations in real world, which are the fundamentals of modern cyber security.

Learning outcomes

On completion of this module, you should be able to:

• Demonstrate knowledge of different cryptographic protocols, techniques, algorithms and implementations that are widely used in protection of confidentiality, integrity, authentication and non-repudiation, and be able to use their knowledge to address real-world security issues
• Fundamentals of security including privacy, integrity, authentication and non-repudiation in internet-connected world
• Concepts, protocols and algorithms of modern cryptographic mechanisms
• Implementations and applications of cryptography in cyber security
• Be able to apply gained knowledge in cryptography in protection of data and user security for real world scenarios
• Critically analyse detailed cryptographic mechanisms for weakness and potential threats pertaining to big data systems
• Be able to analyse the cryptographic requirements for real security issues in data systems
• Apply gained knowledge in cryptographic protocols, algorithms and mechanisms in addressing the security concerns of data systems
• Demonstrate gained knowledge in cryptography in security applications & APIs
• Apply their critical analysis and problem solving skills in the industry for tackling problems and providing solutions for both cyber security and big data services
• Ability to look at things in sufficient detail with critical thinking
• Demonstrable competitiveness in data security protection
• Build confidence in research, development, implementation and maintenance of advanced cyber security systems

Assessment

• 10% Project presentation
• 30% Final coursework report
• 60% In-Class Test

Dissertation

Dissertation

The Dissertation module will equip you with the relevant skills, knowledge and understanding to embark on your own research project.

You will have the choice of three dissertation pathways:

1. A desk based research project that could be set by an organisation or could be a subject of the student's choice
2. A project that involves collection of primary data from within an organisation or based on lab and/or field experiments
3. An Internship within an organisation during which time students will complete a project as part of their role in agreement with the organisation (subject to a suitable placement position being obtained)

By undertaking a dissertation at master's level, you will achieve a high level of understanding in your chosen subject area and will produce a written thesis or project report which will discuss your research in more detail.

Learning Outcomes

On successful completion of this module, you should be able to demonstrate knowledge and understanding of:

• The importance of project planning;
• The importance of a clear hypothesis or research question;
• The ethical implications of research;
• The relevant empirical data and methodologies for data collection or knowledge assimilation for the subject area;
• Methods of data analysis and their suitability for the intended data;
• The areas of expertise or publications of the major individuals or organisations in the subject or business area;
• The previous research or current knowledge in the specific subject or business area;
• Theoretical perspectives relevant to your chosen topic;
• The most effective methods of presentation for data or knowledge;
• Developing a clear, coherent and original research question, hypothesis or business problem in a suitable subject area;
• Synthesising relevant sources (e.g. research literature, primary data) to construct a coherent argument in response to your research question, hypothesis or business problem;
• Analysing primary or secondary data collected by an appropriate method;
• Critically evaluating data collected in context with previously published knowledge or information;
• Engaging in critical debate and argumentation in written work;
• Applying principles of good scholarly practice to your written work;
• Performing appropriate literature searching/business information searching using library databases or other reputable sources;
• Planning a research project and producing a realistic gantt chart demonstrating your intended timelines;
• Synthesising information from appropriate sources;
• Demonstrating rational use of research method tools;
• Selecting and using appropriate investigative and research skills;
• Demonstrating effective project planning skills;
• Finding and evaluating scholarly sources;
• Engaging in critical reasoning, debate and argumentation;
• Demonstrating effective report writing skills;
• Recognising and using resources effectively;
• Successfully managing a project from idea to completion;
• Demonstrating commercial awareness or the impact of knowledge transfer in a business or research environment

Assesement

100% Coursework consisting of:

• 20% Literature review
• 20% Research proposal
• 60% Dissertation report/essay

Principles of Data Science

Principles of Data Science

The aims of this module are to:

• Introduce students to the concepts of data science and their use in Data Analytics Systems
• Enable them to gain theoretical and practical experience in simulating complex data systems involved in a variety of industries including, smart digital systems, Internet of Things, financial industries, and entertainment industries

Learning outcomes

On completion of this module, you should be able to:

• Critical awareness of the challenges caused by the proliferation of data generation processes
• Systematic understanding of the process of extraction of actionable knowledge from data to enable decision making
• Theoretical background in descriptive and inferential statistics for big data
• Machine learning algorithms for classification and pattern analysis in large data sets
• Advanced analytical techniques, such as model building, network graph analysis, outlier detection
• Methods for maximising predictive performance of algorithms and validation techniques
• Analyse common summary statistics and use statistical tests to determine confidence for a hypothesis
• Demonstrate ability to fit a distribution to a dataset and use that distribution to predict event likelihoods
• Examine and evaluate the capabilities of available classification algorithms and be able to select and use suitable for a particular data set
• Integration of knowledge to critically evaluate different scenarios/problems and design practical solutions to data related problems
• Application of knowledge to through programming skills to build predictive and descriptive models for a given dataset utilising available labelled data sets
• Apply creativity and problem solving skills in the industry/research for challenging problems in a timely manner
• Communicate complex problems and associated solutions to specialist and non-specialist audiences
• Evaluate problems and design solutions to those problems through scholarship gained through self-directed study

Assessment

• 40% Coursework
• 20% Group presentation
• 40% Exam

The Collaborative Project

The Collaborative Project

With a multi-talented group of students, you will work on a brief from a real company looking to solve a real social or business problem.

Together with your student team, you will research and build solutions to a business problem, supported by our project tutors, clients and staff. Previous clients include Foster + Partners, Speedo, The London Legacy Development Corporation as well as many other companies, start-ups and charities.

The Collaborative Project provides a means for you to engage in critical enquiry and to be exposed to project-based teamwork in multicultural and interdisciplinary settings. By undertaking this module, you will strengthen your cooperative and collaborative working skills and competencies, whilst raising your awareness and appreciation of cultural and disciplinary diversity and differences.

The Collaborative Project aims to provide you with a hands-on experience of identifying, framing and resolving practice-oriented and real-world based challenges and problems, using creativity and appropriate tools to achieve valuable and relevant solutions. Alongside the collaborative elements of the module, you will be provided with opportunities to network with stakeholders, organisations and corporations, which will give you the experience and skills needed to connect to relevant parties and potentially develop future employment opportunities.

Learning Outcomes

On completion of this module, you will be able to:

• Work effectively in diverse and interdisciplinary teams;
• Undertake and contribute towards a project-based development process;
• Apply critical enquiry, reflection, and creative methods to identify, frame, and resolve issues and problems at hand;
• Identify user and stakeholder needs and value creation opportunities, whilst collecting and applying evidence-based information and knowledge to develop appropriate insights, practices and solutions;
• Identify, structure, reflect on key issues and propose solutions to problems in creative ways;
• Enhance your appreciation for diversity and divergent individual and disciplinary perspectives;
• Be able to provide structured, reflective and critical feedback to peers and other stakeholders;
• Plan and execute a project plan including scope, resources and timing;
• Effectively communicate ideas, methods and results to a diverse range of stakeholders;
• Use multiple, state-of-the-art date media and technologies to communicate with collaborators;
• Make informed, critical and reflective decisions in time-limited situations.

Assessment

100% Coursework consisting of:

• 20% Group project proposal
• 20% Individual reflection
• 30% Final project report
• 30% Project deliverables to the client

As part of your programme, you will have the opportunity to choose one module from another programme within your Institute, or from another Institute at Loughborough University London. For a list of possible second subject modules for your programme, please see the Optional Modules tab or email for more information.

Advanced Big Data Analytics

Advanced Big Data Analytics

The aims of this module are to:

• Introduce the concept of Big Data systems and the challenges posed by such systems
• Introduce the requirement of advanced analytics, processing techniques and architectural solutions to tackle the problems encountered

Learning outcomes

On completion of this module you should be able to:

• Describe the theoretical background of big data, and recognise the need for big data analytics
• Have a critical awareness of machine learning algorithms for data analytics in big data systems
• Describe distributed architectures of big data systems including database technologies used in industrial big data systems
• Understand signal processing techniques for big data systems with advanced matrix manipulation
• Appreciate various visualisation tools and techniques
• Select and apply various machine learning algorithms to a given data set to interpret the data and make necessary predictions
• Demonstrate ability to evaluate and select an appropriate database technology for a given need for big data storage and retrieval
• Analyse the need for visualisation and employ appropriate visualisation tools
• Critically analyse building blocks of a practical big data systems for performance improvement
• Demonstrate programming skills related to data analytics and usage of associated tools
• Formulate creative data solutions that start with cleaning up raw data sets, to discover new patterns that are underlying, to make necessary predictions, utilizing established state-of-the-art tools and techniques
• Develop experimental, analytical and problem solving skills in data driven applications
• Illustrate professional report writing, presentation and communication skills to communicate complex ideas to expert and non-expert audiences
• Develop creative thinking skills to demonstrate ways of solving problems with existing tools

Assessment

• 35% Coursework
• 15% Presentation and Q&A
• 50% Exam

Digital Application Development

Digital Application Development

This module will introduce you to MATLAB programming tools and environments, and will develop your understanding of programming in C++; a basic tool for developing digital systems and applications.

Specific topics covered include C++ programing, objective foundation and advanced programming techniques such as class and structure, MATLAB programming skills and simulation tools.

Learning Outcomes

On completion of this module you should be able to:

• Demonstrate programming skills in C++ and MatLab
• Demonstrate necessary programming techniques to develop digital applications and simulations
• Show knowledge of programming structure and skills in general
• Understand how to use digital data processing tools and functions
• Understand techniques used in developing applications and related numerical simulations
• Understand the concept of advanced techniques of structure and class
• Identify, utilise and optimise tools, algorithms and functions for simulation of digital applications
• Implement functions and algorithms in C++ and MatLab
• Understand the use of advanced techniques of, e.g. class, structure, and pointer
• Demonstrate knowledge of different algorithms, functions and their usage in DSP tool box
• Understand digital technology applications and simulations for digital media and communication systems
• Solve complex problems involving digital processing algorithms
• Identify solutions for practical problems by reasoning, deduction and implementing from idea to final application
• Understand abstract features from complex objects and problems, and develop ideas into algorithms
• Fulfil the research and development requirements in a range of digital systems and digital technologies sectors

Assessment

• 100% coursework

Internet and Communication Networks

Internet and Communication Networks

This module introduces the principles of communication networks and network technologies, including fixed and wireless networking, such as Ethernet, WLANs, mobile broadband networks, as well as basics related to communications, such as FEC coding and modulation. Topics will cover the layers within the Open System Interconnection (OSI) model, including the physical layer (e.g., copper, fibre, wireless, satellite), data link layer (e.g., MAC, 802.11, 802.3), network layer (routing, congestion, QoS, IPv4/v6), transport layer (UDP, TCP, socket programming), and application layer (HTTP, email, VoIP, VoD, streaming, conferencing). The module will also incorporate xDSL and Fibre broadband, P2P networks, and content distribution techniques.

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