Artificial Intelligence with Cyber Security MSc

Postgraduate (1 year full-time)



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Gain an in-depth understanding of artificial intelligence concepts, principles and technologies that affect cyber security. The security of business systems and data is vital, and with most business having an online presence you will learn to implement AI to monitor and protect against cyber attacks.

Find out more about teaching options and studying during COVID-19 in the Entry requirements section, below.

Full description


Once you have completed this course you will be prepared to undertake roles that require programming for data analysis, a knowledge of machine learning, neural networks and deep learning and also apply these skills specifically to cyber security.

If you'd like to continue your studies in research or further education, Anglia Ruskin offers a wide range of full-time and part-time postgraduate research degrees including MPhil or/and PhD in Computer Science, or even a DProf in Science and Technology.

Modules & assessment

Core modules

  • This module focuses on the use of Python programming for data analysis and provides you with the skills needed to complete artificial intelligence projects that are relevant for both industry and research. You will learn how to use Python programming to efficiently store and manipulate data as well as relevant data science tools that will enable you to start your own data analysis. You will be introduced to IPython as an interactive shell to be used as a primary development environment, will learn the basics and advanced features of NumPy (Numerical Python) and will create data visualisations with Matplotlib. This module will also introduce the Pandas library for data analysis. Finally, you will learn how to solve problems by incorporating all the elements reviewed in the module and apply them in different scenarios. This is a hands-on module where you will understand Python concepts through practical lectures and demonstrations and then apply them in a practical session. This module and will provide you with the necessary skills to use Python, as well as its different tools/libraries, to prepare data for data analysis, you are not expected to have previous experience with Python, but it is desirable to have programming experience.
  • Web applications have reshaped business for the better by making e-commerce, online banking, and highly customised customer and partner portals possible. By moving business-critical applications and services like sales, support and purchasing to the Web, organisations have extended the boundaries of the enterprise—opening it up to enhance interaction with customers, suppliers, partners and employees. Web applications also speed and streamline internal processes. In other words, they deliver the benefit businesses are always looking for, from higher employee productivity and lower support costs to increased customer satisfaction and greater revenue. There is a serious consequence to this increased reliance on Web applications: they are inherently insecure and easily compromised. In fact, Gartner/NIST rates between 75 to 92 percent of vulnerabilities now occur in application rather than the network. Vulnerable Web applications not only put network systems and devices at much greater risk, they also offer a direct conduit to confidential customer data such as credit card numbers, account history and health records, as well as to sensitive corporate information. This module through the use of lecture material, online reading tools industry standard tools and practical lab exercises allow you to be able to critically analyse and appraise web applications from a penetrations tester point of view and be able interpret how security vulnerabilities impact web application design and operation. You will learn to evaluate web applications security risk levels and recommend appropriate mitigation controls.
  • Machine learning is a sub-discipline of the Artificial Intelligence that deals with teaching the computer to act without being programmed. In this module you will learn about the tools and algorithms that can be used to create machine learning models. Big data and their economic, legal and ethical aspects are explored, along with data acquisition and pre-processing methods that are used to make these suitable for machine learning algorithms. You will also look into how large data sets should be divided into a training set and a test set and different types of problems that can be solved with machine learning will also be introduced. A range of parametric algorithms such as linear regression, logistic regression, and non-parametric algorithms such as K-Nearest neighbour, decision trees, SVMs, will be discussed. To be able to evaluate a model, a few performance metrics will be explored, the metrics chosen influence how the performance of machine learning algorithms are measured and compared. An important concept that you have to be aware of when training machine learning algorithms is ‘overfitting’, an over fitted model will have a low accuracy and therefore you will learn how to use regularization to avoid overfitting.
  • Develop your skills in preparation to undertake independent research at the MSc level. In particular, you will develop skills to get you started and lay the essential foundations for your Masters Research Project. During the module you will hone your specific research interests whilst recognising time constraints and other practical considerations. These usually set, and limit, the topic, the research approach and study, and the selection of suitable methods. The module will cover: the scientific process, logical thinking and experimental design; critical evaluation of the literature; data analysis using the freely available statistical software R from different perspectives to tackle a range of experiments; and writing a research proposal for an MSc project. You will learn through face-to-face tutorial-style sessions, supplemented by structured reading and exercises. During the module you will be supported in defining your area of study and planning your research project. As part of this process you will be expected to undertake a critical review of the appropriate literature and other information relevant to the proposed project. You will need to give consideration to the logistics required, provide experimental design criteria (where appropriate) at least for the initial stages of the proposed work, and also take account of all health and safety regulations (including COSHH) and appropriate ethical considerations. Designing research projects and writing research proposals and taking responsibility are crucial skills for careers at postgraduate level, where you may have to consider deadlines (time management), ethical issues and assessing risks. Experience of using freely available analytical software also gives you an advantage.
  • Here you will consider how machine learning is being applied to modern cyber security and threat detection. You will be introduced to the tools and techniques in network and software application threat mitigation, and discuss how artificial intelligence is currently, or should be, used to support security analysts in their jobs. The practical sessions of this module are very hands-on and you will learn basic cyber penetration testing techniques and tools, through a series of weekly laboratory tasks. Penetration testing framework tools such as Kali, will be used to test both network and software applications for vulnerabilities. Throughout this module, you will be encouraged to consider how the tools and techniques covered could be applied to ideas for you Major Project. You are not expected to have previous experience with threat detection, but it is desirable to have an understanding of the OSImodel and network protocol handshakes, together with an understanding of how software applications are built. You are also not expected to have previous experience with penetration testing, but would benefit from having used Linux operating systems such as Debian.
  • Deep learning and neural networks have revolutionised numerous fields in recent years. From smartphones and smart watches to cars and even house appliances, electronic devices are increasingly making use of machine learning and neural computing to take decisions, categorise and classify items, learn behaviours, assist us with choices and make prediction. The near future will see an even larger number of “self-learning” devices in almost every aspect of our lives. This module explores two main areas of Intelligent Systems: neural networks and deep learning. You will start analysing the structure of neural networks, from the theoretical aspects to the practical implementations, both biological and artificial. You will then move to the concept of supervised and unsupervised learning and analyse some of the most widely used deep learning methodologies. You will cover some of the main models and algorithms for regression, classification, clustering and decision making processes. The module will include applications of neural computing and deep learning to big data in physical and biological sciences, finance and social sciences. You will use primarily the Python programming language and requires familiarity with basic linear algebra, probability theory, and programming in Python.
  • This module supports students in the preparation and submission of a Master's stage project, dissertation or artefact. The Module provides the opportunity for students to select and explore in-depth, a topic that is of interest and relevant to their course in which they can develop a significant level of expertise. It enables students to: demonstrate their ability to generate significant and meaningful questions in relation to their specialism; undertake independent research using appropriate, recognised methods based on current theoretical research knowledge, critically understand method and its relationship to knowledge; develop a critical understanding of current knowledge in relation to the chosen subject and to critically analyse and evaluate information and data, which may be complex or contradictory, and draw meaningful and justifiable conclusions; develop the capability to expand or redefine existing knowledge, to develop new approaches to changing situations and/or develop new approaches to changing situations and contribute to the development of best practice; demonstrate an awareness of and to develop solutions to ethical dilemmas likely to arise in their research or professional practice; communicate these processes in a clear and elegant fashion; evaluate their work from the perspective of an autonomous reflective learner.


Throughout the course, we’ll use a range of assessment methods to measure your progress. You’ll complete exams, essays, research reports, oral presentations, and a dissertation on a subject of your choice.

Where you'll study

Your faculty

The Faculty of Science & Engineering is one of the largest of the four faculties at Anglia Ruskin University. Whether you choose to study with us full-time or part-time, on campus or at a distance, there’s an option whatever your level – from a foundation degree, BSc, MSc, PhD or professional doctorate.

Whichever course you pick, you’ll gain the theory and practical skills needed to progress with confidence. Join us and you could find yourself learning in the very latest laboratories or on field trips or work placements with well-known and respected companies. You may even have the opportunity to study abroad.

Everything we do in the faculty has a singular purpose: to provide a world-class environment to create, share and advance knowledge in science, technology and engineering fields. This is key to all of our futures.

Where can I study?

Lord Ashcroft Building on our Cambridge campus

Our campus is close to the centre of Cambridge, often described as the perfect student city.

Explore our Cambridge campus

Fees & funding

Course fees

UK students starting 2022/23 (full-time, per year)


International students starting 2022/23 (full-time, per year)


How do I pay my fees?

UK students

You can pay your fees upfront, in full or in instalments – though you won't need to pay until you've accepted an offer to study with us.

How to pay your fees directly

International students

You can pay your fees upfront, in full or in two instalments. We will also ask you for a deposit of £4,000 or a sponsorship letter. Details will be in your offer letter.

Paying your fees

Funding for postgraduate students

It’s important to decide how to fund your course before applying. Use our finance guide for postgraduate students to learn more about postgraduate loans and other funding options.

We offer a fantastic range of ARU scholarships and bursaries, which provide extra financial support while you're at university.

International students

As well as a number of scholarships, we offer an early payment discount. Explore your options:

Entry requirements

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Important additional notes

Whether you're studying entirely online or through a blend of face-to-face and online learning from September 2021, you'll need a computer and reliable internet access to successfully engage with your course. Before starting the course, we recommend that you check our technical requirements for online learning. Our website also has general information for new students about starting university in 2021-22.

Our published entry requirements are a guide only and our decision will be based on your overall suitability for the course as well as whether you meet the minimum entry requirements. Other equivalent qualifications may be accepted for entry to this course, please email for further information.

Teaching options

In response to the COVID-19 pandemic, face-to-face campus teaching (where offered as part of a course) is supported by online learning using our established online learning systems. The number of contact hours varies course by course, and you can contact us for further information. The provision offered is subject to change due to the possibility of further Government restrictions, however we remain committed to delivering face-to-face teaching and ensuring a safe and inclusive environment.

In the event that there are further changes to the current restrictions that are in place due to the pandemic, we may need to move some courses online only at short notice to remain in line with Government guidelines and ensure the continued safety of our students and staff.

International students

We welcome applications from international and EU students, and accept a range of international qualifications.

Whether you're studying entirely online or through a blend of face-to-face and online learning from September 2020, you'll need a computer and reliable internet access to successfully engage with your course. A small number of our courses require additional technical specifications or specialist materials. Before starting the course, we recommend that you check our technical requirements for online learning. Our website also has general information for new students about starting university in 2020-21.

English language requirements

If English is not your first language, you'll need to make sure you meet our English language requirements for undergraduate courses.

Check the standard entry requirements for IELTS requirements for this course.

Improving your English language skills

If you don't meet our English language requirements, we offer a range of courses which could help you achieve the level required for entry onto a degree course.

We also provide our own English Language Proficiency Test (ELPT) in the UK and overseas. To find out if we are planning to hold an ELPT in your country, contact our country managers.

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Get more information

UK & EU applicants

01245 68 68 68

Enquire online

International applicants

+44 1245 68 68 68

Enquire online