Applied Cyber Security / Cibearshlándáil Fheidhmeach

Second Class Honours grade 2 (GPA 2.5 or equivalent) in a Bachelor of Science (Honours) in Bachelor of Science in Computing level 8 or Graduate Diploma or equivalent.

The Masters in Applied Cyber Security is designed to produce highly knowledgeable and skilled graduates to counter the cyber security threat. This course focuses on developing hands-on skills backed by theoretical knowledge. An essential part of the master’s degree is the creation of a body of work presented as a thesis which demonstrates ability in research methods, analytics and report writing. The graduates of this programme will be independent learners, good problem solvers and experienced researchers.

Students on this MSc in Applied Cyber Security will also have the opportunity to take part in Capture-the Flag competitions and through their course work, engage with real world problems with companies so that graduates from the programme have the necessary skills to make a difference in the work place.

The course is suitable for both entrants to a new discipline that require a broader range of taught modules to familiarize themselves with the skills and knowledge of the discipline and for specialist employees who want to up-skill in their specialist areas and require research skills.

MSc in Computing programme is of particular value to holders of a primary degree in computing, IT, or equivalent, working as IT professionals. It is also of value to individuals with a computing degree background who wish to develop their career towards working within a research-oriented environment at a postgraduate level. 

The MSc in Applied Cyber Security will give the skills and knowledge to secure business and personal data and allow graduates to work as security professionals in any of the business and industry sectors.

Former graduates currently work in Deloitte, Integrity360, Fidelity Investiments, Triology, Forcepoint, Rits, Espion, BH-Consulting, Liberty IT, Cobalt Technology, Ward Solutions and larger international companies like Qualcomm, IBM, Deloitte, EY, KPMG, Grant Thornton, Amazon, Google, Microsoft, Ericsson, Zurick Insurance and many more.

Pathway 1: One option is to take three more taught modules and a 30-credit research project.  This path would suit students who are new to the discipline and require more taught modules and a smaller research project.

Pathway 2: The other path is to take a 60 credit research project module route.  This path would suit students who have a background in the discipline and who wish to dedicate more time to the research element of the masters and develop their research capabilities.

The MSc Research Project is a significant amount of work as it comprises from one-third to two-thirds of the credits for the masters.  The research project will have at it core an applied component and incorporate the collecting and analyzing data for improving decision making purposes.

Pathway 1: 60 credits taught with a 30 credit Research project

Semester 1

  • Digital Forensics
  • Secure Communication and Cryptography
  • Network Security

Semester 2

  • Research Skills and Ethics

Electives (Choose any two electives):

  • Biometrics
  • Secure Programming
  • Application Security
  • Business Continuity and Cloud Security
  • Cyber Crime Malware
  • Security Intelligence

MSc Research Project

Students must complete 6 modules and a thesis. All Modules are 10 ECTS Credits. The Research project is 30 ECTS Credits.

Pathway 2: 30 credits taught with a 60 credit Research project

Semester 1

Group Elective 1:

  • Digital Forensics
  • Secure Communication and Cryptography
  • Network Security

Group Elective 2:

  • Cyber Crime Malware
  • Secure Programming
  • Application Security

Group Elective 3:

  • Security Intelligence
  • Business Continuity and Cloud Security
  • Biometrics

Semester 2

MSc Research Project: 90 ECTS credits to be comprised of the Research Project (60 ECTS credits) with the balance made up of 30 credits to be taken from one of the group electives (GE1, GE2 or GE3) in semester 1.

A choice of modules across streams will be offered over an academic year.
Electives will only be available at the discretion of the institute, subject to availability of lecturing staff and sufficient expression of interest from students.

Monday & Wednesday

Apply for this course online at The closing date to apply for this course was 7th June 2019. 

I had noticed that tools in Data Mining could be used to learn clusters and hence to automate my work tasks. I decided to take the course and to go a little deeper . Since I took the course, I have been able to exploit many algorithms to learn patterns and hence assign labels/categories to things in a highly automated way. In addition to the new skills learned I managed to re-kindle skills in Analytics, Software Engineering, Big data and visualisations that I had worked on before, but the course also helped me to hone these skills.

The lectures were delivered online so there was no need to attend in person, but there was always the option to arrange a meeting with a tutor or project supervisor if you so wished or needed that extra support. Each evening is recorded and available on Moodle and hence you never really lose out if you have a conflict. You can listen to the lecture when it suits you. Although, if you wish to ask a question it is best to try to attend on the event.

When combining a day job with a course, it is essential to have a clear line of sight of activities due at the start of each module. The roadmap is very clearly set-out at the beginning of a module and there was a definite plan for the activities. The course is exciting. If your work life involves Spreadsheets, SQL statements, VLOOKUP, PowerPoint charts, then this course is for you. The course allows you to update your skills and knowledge in a field that is currently very popular. Deep Learning, Advanced Analytics, Predictions, Scoring, ETL (extract transform load) and visualisations will be demonstrated to you naturally and understandably. You will gain practical experience, do projects and importantly be able to use these techniques in your day job. The highlight for me was doing the Research Project and taking final delivery of my Thesis.

I enjoyed the course and gained a tremendous benefit from doing it. I feel so much more confident now tackling big data jobs, drawing charts, or using Predictive Analytics.

David Moore,
Graduate Master of Science in Computing (Applied Data Science & Analytics)

TU Code



NFQ Level 9


Master of Science


1 year

Course Type


Mode of Study

Full Time

Method of Delivery


Commencement Date

Week commencing 23/09/2019



Fees (EU)


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