Course Overview

Cybersecurity is one of the most exciting and fastest growing areas of the ICT industry. It is a domain of enormous economic and societal importance, as it is aimed to protect citizens, businesses, and organisations against increasingly complex, damaging, and sophisticated attacks. 

This increasing complexity and level of sophistication requires new means of attack detection, protection, and mitigation, which are addressed by this innovative new programme, the MSc in Computer Science (Adaptive Cybersecurity) offered by the University of Galway. 

Adaptive cybersecurity incorporates state-of-the-art advanced dynamic cybersecurity techniques, algorithms, and frameworks to efficiently protect and mitigate systems and organisations against new emerging threats. 

This 12-month full-time programme provides cutting edge technical training and research opportunities in the emerging area of AI-driven and data analytics-driven cybersecurity. It is a unique offering that is only matched by a small number of European and US-based Universities and builds on the vast research experience and technical skills of renowned, interdisciplinary experts based in the School of Computer Science, University of Galway. 

This programme is aimed at graduates with a primary qualification and / or extensive industry experience in Computer Science or related subject area. It is not a conversion programme but expects students to already be at a very high standard regarding their Computer Science education.

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You may also be interested in one of our other School of Computer Science postgraduate programmes.

Applications and Selections

Applications are made online via the University of Galway Postgraduate Applications System

Please visit Supporting Documents website for detail on what you need to include with your online application.

Who Teaches this Course

  • Dr Malika Bendechache
researcher
Dr Mamoona Asghar
PhD
Lecturer Above The Bar
E: mamoona.asghar@universityofgalway.ie
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researcher
Prof Peter Paul Buitelaar
PhD.
Professor in Data Analytics
Data Science Institute
University of Galway
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researcher
Dr Bharathi Raja Asoka Chakravarthi
BTech., M.Sc., PhD
Lecturer Above The Bar
E: bharathi.raja@universityofgalway.ie
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researcher
Dr. James Duggan
B.E., M.Eng.Sc., Ph.D.
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researcher
DR CONOR HAYES
B.A., M.Sc., Ph.D.
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researcher
Dr Enda Howley
B.Sc, Ph.D
Senior Lecturer
INFORMATION TECHNOLOGY
SCHOOL OF ENGINEERING
& INFORMATICS
IT BUILDING
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researcher
Dr Patrick Mannion
B.Eng, Phd
Lecturer in Computer Science
IT Building
NUI Galway
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researcher
Dr Jawad Manzoor
BIT, MSc, PhD
Lecturer - Contract Type B
Computer Science
University of Galway
Galway
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researcher
Dr Karl Mason
B.Sc., M.Sc., PhD
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researcher
Dr James Mc Dermott
B.Sc., PhD
Lecturer Above The Bar
IT Building 441
University of Galway
Galway
Ireland
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researcher
Prof Michael Madden
B.E., Ph.D., M.I.E.I.
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researcher
Dr IHSAN ULLAH
M.Sc., PhD
Lecturer Above The Bar
IT Building
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Requirements and Assessment

Key Facts

Entry Requirements

Prior qualification

  • This MSc is targeted at high-performing graduates of Level 8 computer science programmes, or Level 8 science/engineering programmes that offer sufficient training in computing.
  • The minimum academic requirement for entry to the programme is a First Class Honours (or equivalent) from a recognised university or third-level college. However, a good Second Class Honours (or equivalent) can be deemed sufficient on the recommendation of the Programme Director.   
  • English language proficiency
    Overall, entry to the MSc Artificial Intelligence requires a minimum IELTS score of 6.5 overall, 6.5 in Writing and no less than 6.0 in any other band. TOEFL: Overall 88, Listening 12–19, Speaking 18–19, Writing 24–26, Reading 13–18. PTE: Overall 61, Writing 61, all other bands no less than 50. 
  • Applicants are required to submit a personal statement outlining: 
    • A summary of your primary degree and its relevance for a successful completion of this programme. We strongly encourage an evidence based approach to highlighting your academic accomplishments. 
    • A summary of your previous capstone projects (e.g., undergraduate final year projects) including an outline of your exact contribution there. We strongly encourage an evidence based approach to outlining your existing technical skills and experience 

  • Please upload a current C.V. 
 

Additional Requirements

Recognition of Prior Learning (RPL)

Duration

1 year, full-time

Next start date

September 2024

A Level Grades ()

Average intake

25

QQI/FET FETAC Entry Routes

Closing Date

No set closing date. Offers made on a continuous basis

NFQ level

Mode of study

ECTS weighting

90

Award

CAO

Course code

1ACS1

Course Outline

Students will collect 90 ECTS during 12 months of full-time studies. The programme covers over two semesters many complementary areas of Cybersecurity, Artificial Intelligence, and Data Analytics, including Intrusion Detection and Malware Analysis, Secure DevOps, Ethics & Data Privacy, Deep Learning, Case Studies in Cybersecurity Analytics, Autonomous Agents and Multi-Agent Systems. Further on, students reinforce their newly gained skills in a project that is completed during the summer. 

During semester 1 students will focus on foundation topics comprising of 5 core modules and one elective module (30 ECTS in total) as follows: 

Module Code   Module Name Core/Elective
CT5165

Principles of Machine Learning

Core
CT5189 Introduction to Cybersecurity Core
CT5191 Network Security & Cryptography Core
CT5190 Societal Impact of AI and Cybersecurity Core

CT5132

Prog. and Tools for AI Core
CT5141 Optimisation Elective
CT5120 Natural Language Processing 1 Elective
CT561 System Modelling and Simulation Elective
CT5105 Tools & Techniques for Large Scale DA Elective

During semester 2 advanced topical areas will be covered, again comprising of 5 core modules and one elective module (30 ECTS in total) as follows: 

Module Code   Module Name Core/Elective

CT5133

Deep Learning

Core
CT5100 Data Visualisation Core
CT5192 Secure DevOps Core
CT5193 Case Studies in Cybersecurity Analytics Core

CT5194

Malware and Intrusion Detection Core
CT5134 Agents, Multi-Agent Systems and Reinforcement Learning Elective
CT5121 Advanced Topics in NLP Elective
CT5113 Web & Network Science Elective
CT5187 Knowledge Representation Elective

Lectures are complemented by weekly labs and tutorials. Assignment work will typically provide 30% of a subject’s overall mark, while the remaining 70% are covered by an end-of-term examination.

Following the semester 2 examination period students will work on a 30 ECTS, 3-month research / capstone project (CT5195), where they showcase their newly gained skills by applying a variety of artificial intelligence and data analytic techniques to solve a real-world cybersecurity problem.

Why Choose This Course?

Career Opportunities

The high global demand in cybersecurity experts is being reflected in a range of career options for example as network security architect, cybersecurity operations analyst or information security analyst.

While our graduates can compete for such jobs, the programme, in particular, will cater for the demand stemming from emerging R&D career paths in cybersecurity that have a strong focus on machine learning and data analytics. These include positions such as an AI security controls architect, cybersecurity data analytics engineer, or cyber intelligence analyst.

Who’s Suited to This Course

Learning Outcomes

Transferable Skills Employers Value

  • Develop skills needed for sustained critical reflection.
  • Enhance skills in the area of problem solving through engagement with difficult organisational and technical (cyber-) security questions.
  • Enhance students’ skills in research, communication, and innovative thinking.
  • Identify the general principles that connect problems and thereby evaluate the strengths and weaknesses of cybersecurity measures in an organisation. [Critical Reasoning]
  • Conduct structured, educated and result-driven research on a known threat, as well as the ability to perceive potential future threats and their mitigations. [Analytical Skills]
  • Communicate difficult ideas in a clear and persuasive manner, while listening to problems/ideas/proposals, and understanding and providing different points of view. [Communication Skills]
  • Look at problems from diverse points of view. [Design and Planning Skills]
  • Identify a security problem and formulated questions relevant to clarifying the threat(s)/issue(s). [Research and Investigation Skills]

Work Placement

Study Abroad

Related Student Organisations

Course Fees

Fees: EU

€8,640 p.a. (including levy) 2024/25

Fees: Tuition

€8,500 p.a. 2024/25

Fees: Student levy

€140 p.a. 2024/25

Fees: Non EU

€27,000 p.a. (€27,140 p.a. including levy) 2024/25

 

Postgraduate students in receipt of a SUSI grant—please note an F4 grant is where SUSI will pay €4,000 towards your tuition (2023/24).  You will be liable for the remainder of the total fee.  An F5 grant is where SUSI will pay tuition up to a maximum of €6,270. SUSI will not cover the student levy of €140.

Note to non-EU students: learn about the 24-month Stayback Visa here

 

Find out More

MSc (Adaptive Cybersecurity) Programme Administrator,
School of Computer Science,
College of Science and Engineering,
University of Galway.
T: +353 91 493 835
E: MScCS-ACS@universityofgalway.ie
www.cs.universityofgalway.ie

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Postgraduate Scholarships