MSc Computer Science with
Cyber Security SCIENCE
COMPUTER
MSc
PREPARINGModule
FOR YOUR
STUDIES
Online Programme
Speci
ication Guide
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Contents:
Security Engineering
Cyber Security Threats
Algorithms and Data Structures
Advanced Programming
Computer Architecture and Operating Systems
Computer and Mobile Networks
Software Engineering
Arti icial Intelligence and Machine Learning
Research Methods
Research Proposal
Independent Research Project
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Security Engineering
15 credits
Module Summary
This module teaches fundamental concepts,
methods, techniques and tools involved in
the development of secure application
systems, from security requirement analysis
and speci ication to design of secure
application systems, secure programming
and security testing.
Module Learning Outcomes
Academic and graduate skills
On successful completion of the module,
students will be able to:• Critically analyse the concept of
security and security threats in the
context of dependable systems,
•
Formulate different types of security
requirements that may be required in a
complex system,
•
Critically evaluate and apply security
design and secure coding principles,
technologies and tools
•
Communicate design decisions for
security problems.
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Subject Content
Indicative Reading
Assessment
Weekly module content:
1. Identity, authentication and
authorisation
2. Security models
3. Origins of vulnerabilities - architectural
and operating system concepts
4. Vulnerability reduction and avoidance
techniques
5. Programming security mechanisms
6. Incident investigation readiness
assessment
•
Elements of P leeger, P leeger,and
Margulies - "Security in
Computing",5th ed. PrenticeHall 2015 may be useful.
Essay/coursework
Written report
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Cyber Security Threats
15 credits
Module Summary
This module will provide the fundamental
aspects of cyber security. Students will
develop an understanding of typical threats,
and become familiar with a range of
technologies that can help to reduce risks.
The module will also explore the leading
edge research challenges, legal and ethical
issues in cyber security.
Module Learning Outcomes
Academic and graduate skills
Successful completion of the module will
demonstrate that students are able to:
• Identify and analyse major threat types
in a variety of systems,
•
Propose an appropriate high-level
security management approach for a
security-sensitive system in a de ined
regulatory environment,
•
Critically evaluate and apply a standard
risk assessment approach/tools to
identify threats to a system,
•
Critically assess the relative merits of
speci ic solution approaches for
particular contexts,
•
Critically discuss leading edge research
in cyber security and the challenges
faced.
•
Critically evaluate the legal and ethical
issues in cyber security.
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Subject Content
Indicative Reading
Assessment
Weekly module content:
1. Computer security major threat types
2. Hacker techniques
3. Computer security technology
4. Security policy, including data
classi ication
5. Network scanning
6. Cyber attacks and cyber detective
7. Forensic evidence
•
William (Chuck) Easttom, Computer
Security Fundamentals, 3rd Edition,
Pearson IT Certi ication, 2016.
Essay/coursework
Written report
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Algorithms and Data Structures
15 credits
Module Summary
The aim of this module is to provide students
with techniques for using some algorithms
and their associated data structures. This
includes the concept of computational
thinking; the theoretical underpinnings of
Computer Science; programming including
data types, control structures, methods,
inheritance, arrays, graphics and the
mechanics of running and testing;
algorithms, their complexity and
implementation in programs; the application
of these ideas in a practical context.
Module Learning Outcomes
Academic and graduate skills
Successful completion of the module will
demonstrate that students are able to:• Express a problem solution
algorithmically using pseudocode
•
Analyse the time complexity of an
algorithm
•
Construct computer programs to
implement algorithms
•
Test a computer program against the
speci ication.
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Subject Content
Indicative Reading
Assessment
Weekly module content:
1. Java building blocks: data types,
operators and expressions
2. Program controls, methods and classes
3. Inheritance, JavaFX and packages
4. Pseudo-code conventions and algorithm
analysis
5. Linear data structures and sorting
6. Hash table and hash functions
7. Trees, tree algorithms and graphs
•
Quentin Charatan & Aaron Kans, Java in
two semesters, 4th edition, Springer,
2019.
•
Cormen et al. Introduction to
Algorithms, 3rd edition, MIT Press,
2009
Essay/coursework
Practical programming report (30%
weighted)
Open examination
Limited time open exam (70% weighted)
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Advanced Programming
15 credits
Module Summary
This module aims to build on the concepts of
programming from the Algorithms and Data
Structure module and provide students with
advanced programming concepts such as ile
manipulation, event driven programming,
multithreaded programming and the use of
packages and documentation. The module
also explores how to program for big data
analysis, and discusses the social context of
computing: social impact of computers and
the Internet; professionalism, codes of ethics,
and responsible conduct; copyrights,
intellectual property, and software piracy.
Module Learning Outcomes
Academic and graduate skills
Successful completion of the module will
demonstrate that students are able to:
• Demonstrate critical understanding of
the theory and application of advanced
programming techniques
•
Design and implement programs for
real world problems
•
Communicate design decisions for the
selection, storage and manipulation of
data
•
Critically evaluate the legal and ethical
impact of software developments
within real world contexts
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Subject Content
Indicative Reading
Assessment
Weekly module content:
1. Data types, data collections, decisions
and control structures
2. Event driven programming
3. Multithreaded programming
4. Data storage and processing
5. Statistics, plotting and visualisation
6. Regression and clustering
7. Legal and ethical issues
•
McKinney, Wes: Python for Data
Analysis: Data Wrangling with Pandas,
NumPy, and IPython, 2nd edition,
O'Reilly Media 2017.
Essay/coursework
Coursework
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Computer Architecture and Operating Systems
15 credits
Module Summary
The module aims to provide students with an
understanding of the concepts of modern
computer architectures and system software.
This module starts with an overview of
computer architecture, then progresses to
topics on how computer systems execute
programs, store information, and
communicate. It will provide the principles,
design and implementation of system
software such as operating systems.
Module Learning Outcomes
Academic and graduate skills
On successful completion of the module,
students will be able to:• Recognise the main components of a
typical computer, analyse and
communicate their individual
behaviour, as well as their interactions,
•
Identify the main components of an
operating system (OS), analyse and
communicate the structure and
behaviour of OS components in
isolation, as well as their interactions,
•
Apply the principles of resource
management and concurrency to
analyse the main design problems at
the Operating System level, and
critically evaluate the approaches taken
by modern-day operating systems in
solving them,
•
Critically evaluate security risks in
operating systems and the role
operating systems can and should play
in establishing security.
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Subject Content
Indicative Reading
Assessment
Weekly module content:
1. Computer architecture and processor
principles
2. Operating systems and their
architectures
3. Processor management:
multiprogramming, scheduling,
synchronisation and communication
4. Memory management: basic techniques,
virtual memory, paging and
segmentation
5. Device management: drivers and storage
management
6. File management: structure, protection
and integrity
7. Performance analysis, system
administration, and analysis of popular
operating systems
•
Stallings, W. Computer Organization
and Architecture: Design For
Performance (8th Edition) Pearson
2010.
•
Silberschatz A., Galvin P.B., and Gagne
G. Operating System Concepts (8th ed.)
Wiley 2009.
Open Examination
Limited time open exam
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Computer and Mobile Networks
15 credits
Module Summary
The module aims to provide a sound
understanding of the Internet architecture,
protocols and technologies. This includes
discussion of modern computer networks
and the Internet, network architectures,
communication protocols and their design
principles, the components and layers of the
TCP/IP network model used on the Internet
from the physical layer to applications,
wireless and mobile networks; network
security issues and networking standards.
These are presented together with their realworld applications. The module will also
cover the social, privacy and copyright issues
related to computer networks and the
Internet.
Module Learning Outcomes
Academic and graduate skills
Successful completion of the module will
demonstrate that students are able to:
• Critically analyse the core concepts in
modern computer networks such as
LANs and WANs, network architecture,
communication protocols and their
design principles, the layered
organisation of computer networks,
and mobile networks,
• Apply network concepts and design
principles, design/communicate and
implement a networked application,
• Critically evaluate and apply tools for
computer network performance
analysis,
• Critically evaluate network security
techniques,
• Critically evaluate the legal and ethical
impact of computer networks and
Internet.
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Subject Content
Indicative Reading
Assessment
Weekly module content:
1. Computer networks and their
applications
2. The physical layer and data link layer,
LAN and WAN
3. The network layer and IP
4. The transport layer and TCP
5. The application layer, DNS, email and FTP
6. Wireless and mobile networks
7. Network security
•
James Kurose & Keith Ross, Computer
Networking: A Top-Down Approach,
7th Edition, Pearson, 2017.
Essay/coursework
Report with an executive summary
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Software Engineering
15 credits
Module Summary
This module focuses on designing and
building software systems, which these days
are often large, complex and long-lived. They
are worked on by teams of engineers, and
changed constantly over their lifetimes. We
will look at principles and patterns of
software design, where to apply them, and
how they may inform our design choices. We
will also look at techniques for ensuring that
systems you build behave correctly. We show
how the application of these makes it
possible to evolve systems effectively in a
rigorous way.
Module Learning Outcomes
Academic and graduate skills
On successful completion of the module,
students will be able to:• Investigate and analyse a problem,
write a software requirement
speci ication and design blueprint
expressed in UML which provides a
basis for code generation,
• Apply a range of design patterns and
principles to solve particular design
problems,
• Apply a range of refactoring techniques
to improve code quality
• Critically evaluate and apply a range of
tools and techniques for automated
software testing, including test-driven
development,
• Manage risk in making changes to an
existing software system through
rigorous engineering practices,
• Critically evaluate the appropriateness
of different software engineering
techniques/tools in different
circumstances, and on the quality of the
design of an application.
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Subject Content
Weekly module content:
1. Software development process
2. Requirement capture and modelling
3. Requirement analysis and speci ication
4. High-level and lower level design
5. Design patterns and state machines
6. Refactoring and software testing
7. Software risk and quality management
Indicative Reading
Assessment
•
Bennett, Simon; McRobb, Steve;
Farmer, Ray, Object-Oriented System
Analysis and Design, 4th Ed, McGraw
Hill, 2010.
•
Sommerville I., Software Engineering,
9th edition, Addison Wesley, 2015.
Essay/coursework
Practical software modelling report (30%
weighted)
Open Examination
Limited time software engineering exam
(70% weighted)
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Arti icial Intelligence and Machine Learning
15 credits
Module Summary
This module will explore the ield of arti icial
intelligence and study the principal ideas and
techniques in three core topic areas: solving
problems by searching, logic, and machine
learning. It will help students to develop
practical skills in AI problem-solving and to
understand the legal and ethical implications
of AI for business and society.
Module Learning Outcomes
Academic and graduate skills
On successful completion of the module,
students will be able to:• Critically analyse the principal ideas
and techniques of Arti icial Intelligence,
•
Apply AI search to solve problems that
may be represented as states,
transitions and goals,
•
Design logical systems that are able to
represent knowledge and make
decisions,
•
Apply machine learning techniques to
create AI agents that can learn from
observed data,
•
Critically evaluate the societal impact
of AI including legal and ethical issues.
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Subject Content
Indicative Reading
Assessment
Weekly module content:
1. Arti icial intelligence and its application
areas
2. Basic AI search algorithms
3. More advanced AI algorithms
4. Basics of logical systems
5. More advanced topics in provisional
logical systems
6. Overview of the three main types of
machine learning: supervised,
unsupervised and reinforcement
7. Theory and examples of supervised
learning on a range of methods
•
Stuart Russell and Peter Norvig,
Arti icial Intelligence: A Modern
Approach (3rd ed. 2009)
Open Examination
Limited time exam
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Research Methods
15 credits
Module Summary
This module introduces a range of research
methods and types of research projects. You
will learn how to formulate a research
question and to ind answers using both
quantitative and qualitative methods.
Module Aims
The module aims to introduce the student to
a range of possible approaches to research
and types of individual research project that
they may undertake. Students will have the
opportunity to formulate research questions
appropriate to an area of interest and to
evaluate the relationship between question,
methodology and method.
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Module Learning Outcomes
Indicative Reading
Academic and graduate skills
On successful completion of the module,
students will be able to:• Formulate potential research questions
appropriate to an area of interest
• Consider the role of theory in research
• Outline basic methods of conducting
and analysing quantitative and
qualitative research
• Evaluate different methods of
investigating an area of research
interest and consider the nature of the
relationship between research
question, methodology and method
• Critically assess the key characteristics
of qualitative and quantitative research
methods
• Consider the nature of different types
of independent study; a study in a
workplace setting, a non-work-based
literature based study and a non-workbased study using primary or
secondary data
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Assessment
Saunders, M.L. and Lewis, P., 2009. P.
and Thornhill, A.(2009). Research
methods for business students, 4.
Wellington, J. and Szczerbinski, M.,
2007. Research methods for the social
sciences. A&C Black.
Cameron, S. and Price, D.,
2009. Business research methods: a
practical approach. Kogan Page
Publishers.
Hart, C. 2005 Doing your masters
dissertation. Sage,.
Silverman, D., 2011. Interpreting
qualitative data: A guide to the
principles of qualitative research.
Essay/coursework
2500 word assignment
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Research Proposal
15 credits
Module Summary
The research proposal (RP) is an extended
research proposal for the students’ inal
Individual Research Project (IRP). The
module is designed to ensure that students
have planned their IRP in suf icient depth
before they undertake their inal study. The
module has been designed to give students
the lexibility of developing a proposal which
explores a work based problem or one that is
more driven by indings in the literature.
Module Learning Outcomes
Academic and graduate skills
On successful completion of the module,
students will be able to:• Identify an individual research project
within the area of specialism of the
degree programme,
•
Apply knowledge of research
philosophy and methods to justify an
appropriate approach for a speci ic
research question,
•
Critically analyse signi icant bodies of
literature in the chosen topic area to
justify an area of research,
•
Develop a research plan for a
researchable problem,
•
Identify and address ethical issues
associated with a speci ic piece of
research, and attain ethical approval
for the project.
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Indicative Reading
• Projects in computing and information
systems: a student's guide. Pearson
Education. C.W Dawson. 2009
• Research Design, Qualitative,
Quantitative and Mixed Methods
Approaches. John W Creswell. Fourth
edition. SAGE publication, 2014
• Writing for computer science, 2nd edn.
Zobel, J. Springer 2004
• Dissertations and Project Reports : A
Step by Step Guide. Cottrell, Stella,
2014. Basingstoke : Palgrave
Macmillan. Palgrave Study Skills. Web.
Assessment
Essay/coursework
Research proposal report
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Independent Research Project
30 credits
Module Summary
The 30 credit Individual Research Project
(IRP) builds on the Research Proposal
module (which is a pre-requisite module)
where students will have de ined and
developed a plan for a researchable question
within the area of specialism of the degree
programme.
The IRP is the implementation and the writeup of the results of this plan. It provides an
opportunity to develop understanding and
skill in the methods and techniques of
research in Computer Science, ranging from
software or hardware engineering needed
for implementation-based investigations to
the scienti ic method of hypothesis
generation and experiment, or other
appropriate and rigorous methods
depending on the topic of the project.
By undertaking a longer piece of sustained
research and writing, students will
demonstrate: critical analytical skills; ability
to gather and synthesise literature and/or
data from a range of sources; writing skills;
subject-speci ic knowledge. As a self study
module, they will also draw on the skills they
have acquired through their whole degree,
including self-management, working to
deadlines, and subject knowledge.
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Module Learning Outcomes
Indicative Reading
Assessment
Academic and graduate skills
On successful completion of the module,
students will be able to:• Critically evaluate and apply new
techniques and tools,
•
Develop artefact, as appropriate,
serving the purpose of the experiment,
•
Apply knowledge of research
philosophy and methods to undertake
empirical research involving collection
of primary data (where appropriate)
•
Undertake secondary analysis of
existing data and information (where
appropriate)
•
Critically analyse signi icant bodies of
literature in the chosen topic area
particularly in the context of the
research indings
•
Communicate complex computational
problems and their solutions in wellpresented written format.
•
Dawson, CW, 2009, Projects in
Computing and Information Systems: a
student's guide, 2nd edition, Pearson
Prentice Hall
•
Zobel, J, 2014, Writing for Computer
Science, 3rd edition, Springer
University - project
Research project report
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