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Computer Science – Adaptive Cybersecurity (MSc)
MSc (Computer Science – Adaptive Cybersecurity)
College of Science and Engineering, School of Computer Science- Title of Award
- Master of Science
- Course Code
- 1ACS1
- Average Intake
- 25
- Delivery
- On Campus
- NFQ
- Level 9
- Award Type
- Major
- Next Intake
- September 2025
- Duration
- 1 year, full-time
- ECTS Weighting
- 90
Why Choose This Course?
Course Information
Who is this course for?
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.
What will I study?
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:
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 |
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.
Curriculum Information
Curriculum information relates to the current academic year (in most cases).Course and module offerings and details may be subject to change.
Glossary of Terms
- Credits
- You must earn a defined number of credits (aka ECTS) to complete each year of your course. You do this by taking all of its required modules as well as the correct number of optional modules to obtain that year's total number of credits.
- Module
- An examinable portion of a subject or course, for which you attend lectures and/or tutorials and carry out assignments. E.g. Algebra and Calculus could be modules within the subject Mathematics. Each module has a unique module code eg. MA140.
- Optional
- A module you may choose to study.
- Required
- A module that you must study if you choose this course (or subject).
- Semester
- Most courses have 2 semesters (aka terms) per year.
Year 1 (90 Credits)
RequiredCT5189: Introduction to Cybersecurity
CT5189: Introduction to Cybersecurity
Semester 1 | Credits: 5
The introductory module covers the importance of cybersecurity by considering the comprehensive overview of all cybersecurity categories. It will provide learners with a foundation of advanced topics in cybersecurity through the theoretical and practical aspects of Cybersecurity. Learners will develop a strong understanding of the current cybersecurity landscape and best practices for protecting against cyber-attacks. It will also cover the importance of risk management. By the end of this module, learners will have the knowledge and skills to comprehend various types of cyber attacks and assess the security of existing systems.
(Language of instruction: English)
Learning Outcomes
- Discuss and explain the fundamentals of cybersecurity including the various types of cyber-attacks, attack vectors, and the strategies to mitigate them.
- Analyse different security standards, frameworks, and best practices such as NIST RMF, ISO27001, SOC 2-Type 2
- Demonstrate analytical and critical thinking skills to evaluate and assess the security postures of existing systems.
- Identify potential vulnerabilities in existing systems to design secure information systems.
- Discover and apply effective risk management and incident response strategies considering risk governance and threat intelligence.
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5189: "Introduction to Cybersecurity" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5190: Societal impact of AI and Cybersecurity
CT5190: Societal impact of AI and Cybersecurity
Semester 1 | Credits: 5
The module is designed to provide learners with a comprehensive understanding of the social, ethical, and legal implications of advancements in AI and cybersecurity. Throughout the module, learners will be exposed to various real-world examples, case studies, and discussions to understand the impact of AI and cybersecurity on data, society, organizations, humans, and privacy laws. The course will also equip learners with the knowledge and skills to critically evaluate the social, ethical, and legal implications of AI and cybersecurity and to contribute to the development of responsible and sustainable AI and cybersecurity practices.
(Language of instruction: English)
Learning Outcomes
- Identify the social, ethical, and legal implications of AI and cybersecurity advancements concerning data, society, organizations, and individuals.
- Analyse the intersection of AI and cybersecurity, and its impact on privacy, security, and data governance.
- Review the legal frameworks for AI and cybersecurity by evaluating the role of governance in shaping trustworthy AI and cybersecurity.
- Define the critical appraisals of the moral implications of AI and cybersecurity in society and organisations.
- Evaluate responsible, sustainable AI and cybersecurity practices.
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
- MICHAEL SCHUKAT 🖂
- DEIRDRE KING 🖂
- GERALDINE HEALY 🖂
- Ihsan Ullah 🖂
- Mamoona Asghar 🖂
- Malika Bendechache 🖂
Note: Module offerings and details may be subject to change.
RequiredCT5191: Network Security & Cryptography
CT5191: Network Security & Cryptography
Semester 1 | Credits: 5
The module is designed to provide learners with a comprehensive understanding of the principles and practices of securing networks and data through firewalls, VPNs, secure communication protocols, and cryptography. Throughout the module, learners will have the opportunity to apply their knowledge through hands-on exercises and projects, such as configuring firewall rules, implementing encryption, and simulating security breaches in network security.
(Language of instruction: English)
Learning Outcomes
- differentiate between and apply fundamental networking concepts and technologies of network security.
- perform network threat and vulnerability assessments of a given system architecture.
- analyse, adopt, and integrate State-of-Art secure communication protocols in computer networks.
- reflect on the role of cryptography in securing data and communication.
- differentiate between and practically apply fundamental cryptographic concepts, algorithms, and frameworks.
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5191: "Network Security & Cryptography" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5165: Principles of Machine Learning
CT5165: Principles of Machine Learning
Semester 1 | Credits: 5
Machine Learning is concerned with algorithms that improve their performance over time, as they are exposed to new data. This module introduces learners to the different categories of machine learning tasks and provides in-depth coverage of important algorithms for tackling them. Its focus is on the theory underlying ML algorithms. In addition, the learners gain experience of implementing algorithms from scratch, as well as using ML software tools to select and apply these algorithms in applications, and they evaluate and interpret the results.
Topics include:
1. Overview of Machine Learning & Major Categories of Task
2. Supervised Learning Principles and Information-Based Learning
3. Similarity-Based Learning
4. Evaluating Classifier Performance, Practical Advice, and Some Machine Learning Tools
5. Linear Regression in One and Multiple Variables
6. Linear Classifiers with Hard and Soft Thresholds
7. Probabilistic Machine Learning
(Language of instruction: English)
Learning Outcomes
- Define Machine Learning and explain what major categories of learning tasks entail
- Demonstrate how to apply the machine learning and data mining process to practical problems
- Explain and apply algorithms including decision tree learning, instance-based learning, probabilistic learning, linear regression, logistic regression, and others
- Given a dataset and task to be addressed, select, apply and evaluate appropriate algorithms, and interpret the results
- Discuss ethical issues and emerging trends in machine learning.
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5165: "Principles of Machine Learning" and is valid from 2025 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5132: Programming and Tools for AI
CT5132: Programming and Tools for AI
Semester 1 | Credits: 5
This module is about programming and computational tools required for artificial intelligence. It uses the Python language as the main vehicle, but focusses on conceptual material rather than just the language itself. It moves fast through introductory Python workings. It covers several important Python libraries in detail, especially for numerical computing, machine learning, plotting, graphs. It discusses approaches to building re-usable, high quality code but not software engineering per se. It also visits some extra topics such as version control and introduction to the R language for statistics. This is the "classroom" version of the module, which uses a "flipped classroom" delivery. Students consume video lectures in advance, and in the contact hours we have quizzes, discussion, and practical exercises. There is an "online" version of this module also, which is bonded to this, and has the same content and assessment.
(Language of instruction: English)
Learning Outcomes
- Read and write simple Python programs, e.g. for data munging, with a high degree of comfort.
- Use R for simple statistics and data exploration.
- Use numerical Python libraries for manipulation, input/output, visualisation of numerical data using Numpy array types.
- Use essential tools for AI, including libraries for data gathering, numerical computing, machine learning, combinatorial programming, and modelling networks.
- Plan/design a program using any of the above facilities; test it; document it; execute it locally or in the cloud as appropriate.
Assessments
- Written Assessment (50%)
- Continuous Assessment (50%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
- MICHAEL MADDEN 🖂
- MICHAEL SCHUKAT 🖂
- DEIRDRE KING 🖂
- GERALDINE HEALY 🖂
- JAMES MCDERMOTT 🖂
- Bharathi Raja Asoka Chakravarthi 🖂
Reading List
- "Think Python" by Allen Downey
- "A Whirlwind Tour of Python" by Jake Vanderplas
Note: Module offerings and details may be subject to change.
RequiredCT5195: Adaptive Cybersecurity Project
CT5195: Adaptive Cybersecurity Project
15 months long | Credits: 30
In this module the student demonstrates their ability of carrying out an in depth analysis, problem-solving, and reporting of a cybersecurity problem using machine learning and data analytics techniques.
(Language of instruction: English)
Learning Outcomes
- Apply a variety of artificial intelligence (AI) and data analytic (DA) techniques to solve a real world cybersecurity problem.
- Identify a cybersecurtiy problem and design an AI/DA based solution.
- Conduct and report on exploratory analysis of the problem domain.
- Produce an in-depth report (thesis) describing the problem, the methodologies and approaches to solving it.
- Demonstrate that they can research, apply, and evaluate state-of-the-art techniques in adaptive cybersecurity.
Assessments
- Research (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5195: "Adaptive Cybersecurity Project" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5193: Case Studies in Cybersecurity Analytics
CT5193: Case Studies in Cybersecurity Analytics
Semester 2 | Credits: 5
As part of the applied learning opportunities for learners, this module incorporates learning from lectures by invited speakers from academia and industry as well as through team-based and research-oriented elements. Through the integration of curriculum content, the purpose of the module is to provide students real world case studies as well as a chance to complete an applied research and development project on cybersecurity-specific research problems. Learners will work as part of small focused teams on this targeted interdisciplinary project integrating AI / ML with Cybersecurity. As a team, learners will investigate the literature in a systematic manner and then implement and analyse a solution for identified problem statement. As a result, this module will also enable learners to produce a research paper on current topics relevant to industry practices in cybersecurity.
(Language of instruction: English)
Learning Outcomes
- define and explain research methodologies.
- discuss key practical challenges in cybersecurity and gaining deep knowledge from real-world case studies from industry and current open research questions from academia.
- improve skills while navigating through, and critically examining, the scientific literature and resources (tools and datasets) and prepare strategies to meet the identified challenges of research-oriented team-based projects.
- analyse, manage, and conduct research in an ethical and methodologically sound manner while keeping in mind social and legal contexts.
- complete an applied Research and Development (R&D)project to deliver a solution to an authentic cybersecurity problem through the integration and application of acquired knowledge, skills, and competencies.
- communicate the outline, scope, intended deliverables, and outcomes of the R&D project using both verbal and written skills.
Assessments
- Continuous Assessment (100%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5193: "Case Studies in Cybersecurity Analytics" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5100: Data Visualisation
CT5100: Data Visualisation
Semester 2 | Credits: 5
Visualisation is a fundamental technique in presenting the properties of data and the results and evaluation of data analytical processes. For a data visualisation to be successful the analyst needs to have considered the properties of the data, the information to be communicated, the mode of visualisation delivery and the expectations of the audience. This module takes a practical approach to introducing learners to the strengths and weaknesses of human perception, and the use of best practices to represent complex and large data stories using visual primitives. The module demonstrates the role of visualisation in exploratory data analysis and its fundamental role in explaining data analytical outcomes. The module emphasises the need to communicate clearly, while adhering to the ethical requirement to present data-derived information truthfully and without bias. The examples covered during the module have been generated using the R language. However, students can use other languages and visualisation applications in their assignment work.
(Language of instruction: English)
Learning Outcomes
- Describe the basic design principles underlying human perception, color theory and narrative
- Analyse the effectiveness of different visual elements in communicating analytical information
- Select the best visualisation strategy to use for different exploratory and explanatory scenarios
- Execute different types of data visualisations for use in various exploratory and explanatory scenarios
- Carry out basic data preprocessing and wrangling necessary to produce effective visualisations
- Discuss the ethical issues of representing data and information truthfully when creating a visualisation
- Critically evaluate data visualisations produced by other people
Assessments
- Written Assessment (65%)
- Continuous Assessment (35%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "R Graphics Cookbook" by Winston Chang
Publisher: O'Reilly - "ggplot2" by Hadley Wickham
ISBN: 9783319242750.
Publisher: Springer - "Information Visualization" by Colin Ware
ISBN: 9780123814647.
Publisher: Elsevier - "Now You See it" by Stephen Few
ISBN: 9780970601988.
Publisher: Analytical Press - "The Visual Display of Quantitative Information" by Edward R. Tufte
ISBN: 9781930824133.
Publisher: Graphics Press
Note: Module offerings and details may be subject to change.
RequiredCT5133: Deep Learning
CT5133: Deep Learning
Semester 2 | Credits: 5
This is an advanced module in machine learning, focusing on neural networks (NNs), deep NNs, and connectionist computing. Students learn about the basic principles and building blocks of deep learning, and how to implement a deep neural network ‘from scratch’. They also learn about software libraries and tools, and gain experience of applying deep learning in a range of practical applications. The module includes substantial practical programming assignments.
This module is intended for students who have completed a first course in machine learning, and already have a good grounding in supervised learning topics including: classification and regression; evaluation of classifiers; overfitting and underfitting; basic algorithms such as k-nearest neighbours, decision tree learning, logistic regression, and gradient descent.
(Language of instruction: English)
Learning Outcomes
- Explain key Machine Learning concepts that relate to Deep Learning
- Explain the operation of feed-forward neural networks and the back-propagation algorithm
- Describe, implement and apply key features of deep neural networks
- Implement NNs for supervised machine learning tasks, from first principles and (separately) using modern libraries and frameworks
- Choose, explain and implement: (a) recurrent and other NN architectures for sequential data; (b) self-supervised NN architectures for unlabelled data; (c) supervised NN architectures for representation learning
- Discuss ethical issues, limitations, and emerging trends in deep learning.
Assessments
- Written Assessment (60%)
- Continuous Assessment (40%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
- MICHAEL MADDEN 🖂
- DEIRDRE KING 🖂
- GERALDINE HEALY 🖂
- JAMES MCDERMOTT 🖂
- Bharathi Raja Asoka Chakravarthi 🖂
Note: Module offerings and details may be subject to change.
RequiredCT5192: Secure DevOps
CT5192: Secure DevOps
Semester 2 | Credits: 5
This module will enable learners to gain expertise in the end-to-end secure software development i.e., from project specification to implementation phases. It facilitates the learners in understanding and practicing secure software development lifecycles (DevSecOps) using different libraries/APIs and tools. It covers the best practices for identifying potential threats, implementing cybersecurity measures throughout the software development lifecycle, secure coding principles and techniques for application development, and techniques for verifying and validating the security of software systems.
(Language of instruction: English)
Learning Outcomes
- Analyse software design and identify potential threats.
- Implement a secure software lifecycle, focusing on secure application development.
- Apply programming concepts to evaluate security measures and identify vulnerabilities in an ethical manner.
- Utilise programming libraries/API and its associated functionality to perform DevSecOps.
- Compare, contrast, and critically appraise the effectiveness of different and alternate security measures.
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5192: "Secure DevOps " and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
RequiredCT5194: Malware and Intrusion Detection
CT5194: Malware and Intrusion Detection
Semester 2 | Credits: 5
This module is designed to provide a comprehensive security understanding that requires a multi-layered approach, including threat detection, incident response, penetration testing and malware analysis. It covers the topics that provide a holistic understanding of how to protect an organization from cyber-attacks. Techniques and tools used to detect and identify potential threats, including signature-based detection, heuristic detection, and behavioural analysis. This module assists in improving an organization's security posture and best practices for implementing and maintaining a comprehensive security system, including compliance with industry standards and regulations. This module is intended for learners to deepen their knowledge and skills in identifying, analysing, and mitigating cyber threats at the organisational level.
(Language of instruction: English)
Learning Outcomes
- Discuss the concepts, techniques, and tools of malware and intrusion detection for anticipating and preparing for emerging threats.
- Analyse common malware threats and their detection and prevention techniques.
- Examine different intrusion detection tools and techniques for network and system security including Firewall, IDS, SIEM.
- Explore the role of cyber threat intelligence and various intelligence frameworks such as Cyber Kill Chain and MITRE ATT&CK in overall security architecture.
- Implement advanced incident response procedures for handling security breaches.
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5194: "Malware and Intrusion Detection" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
OptionalCT5141: Optimisation
CT5141: Optimisation
Semester 1 | Credits: 5
This module covers optimisation -- "the science of better". Optimisation is used in a huge variety of applications, including: finding time-saving transport routes; scheduling exams without conflicts; reducing weight and cost in engineering design; designing portfolios of financial investments; finding numerical data models with low expected error; and many more. In this module we will aim to understanding a broad range of applications and a unifying view of the field, including four main types of methods: (1) exact methods for constrained optimisation (2) constructive heuristics (3) gradient descent (briefly) (4) metaheuristics. We will use labs for practical implementations, writing our own optimisation programs from scratch in Python and also using state-of-the-art libraries.
(Language of instruction: English)
Learning Outcomes
- Compare and contrast different algorithms and algorithm types (metaheuristics, constrained optimisation, gradient descent, constructive heuristics) and state their advantages and disadvantages for specific problems
- State common real-world applications of optimisation algorithms
- Implement a variety of metaheuristic algorithms, linear programming / constrained optimisation algorithms, and constructive heuristics, either from scratch or using library code, and interpret outcomes in a business or science application context
- Design novel algorithms or algorithm varieties to suit specific problems
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Essentials of Metaheuristics" by Sean Luke
- "Programming Collective Intelligence" by Toby Segaran
Note: Module offerings and details may be subject to change.
OptionalCT5120: Introduction to Natural Language Processing
CT5120: Introduction to Natural Language Processing
Semester 1 | Credits: 5
Natural Language Processing (NLP) is concerned with the automatic analysis, interpretation and annotation of textual data. Applications of NLP are in the extraction of information from text, linking text to databases or other structured knowledge, classification, summarization, translation and generation of text, etc. This module introduces students to the field of NLP, including linguistic, statistical and machine learning foundations, primary challenges and approaches to the syntactic and semantic analysis of textual data, and applications in summarization, chatbot development, knowledge extraction and opinion mining. The course ends with a discussion of ethical aspects in NLP.
(Language of instruction: English)
Learning Outcomes
- Ability to explain the various levels of linguistic structure relevant to NLP.
- Ability to use standard algorithms for basic NLP analysis
- Gain practical knowledge of and experience in the use of NLP toolkits
- Ability to explain a selection of theoretical principles behind core NLP applications.
- Ability to apply NLP algorithms, toolkits and applications to tasks in AI, Data Analytics and other related application areas.
Assessments
- Written Assessment (50%)
- Continuous Assessment (50%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5120: "Introduction to Natural Language Processing" and is valid from 2025 onwards.Note: Module offerings and details may be subject to change.
OptionalCT561: Systems Modelling and Simulation
CT561: Systems Modelling and Simulation
Semester 1 | Credits: 5
Simulation is a quantitative method used to support decision making and predicting system behaviour over time. This course focuses the system dynamics approach. The course covers the fundamentals of simulation, and describes how to design and build mathematical models. Case studies used include: software project management, public health policy planning, and capacity planning.
(Language of instruction: English)
Learning Outcomes
- Define the aim of Simulation and its role in the decision making process for complex systems
- Distinguish between the two feedback types: positive and negative
- Demonstrate how to apply the system dynamics approach to areas including public health, software engineering management and capacity planning.
- Explain and apply numerical integration methods to solve simulation problems.
- Given a simulation problem, formulate a model, test the structure and equations, and perform detailed sensitivity analysis on the impact of a range of policy options
- Build, test and evaluate models using Vensim.
- Appreciate the differences between continuous and agent-based simulation
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT561: "Systems Modelling and Simulation" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
OptionalCT5105: Tools and Techniques for Large Scale Data Analytics
CT5105: Tools and Techniques for Large Scale Data Analytics
Semester 1 | Credits: 5
Large-scale data analytics is concerned with the processing and analysis of large quantities of data, typically from distributed sources (such as data streams on the internet). This module introduces students to state-of-the-art approaches to large-scale data analytics. Students learn about foundational concepts, software tools and advanced programming techniques for the scalable storage, processing and analysis of high- volume and high-velocity data, and how to apply them to practical problems.
** This module uses Java as programming language. Knowledge of Java is a prerequisite for participation in this module. **
Planned topics include: Definition of large-scale computational data analytics; Overview of approaches to the processing and analysis of high volume and high velocity data from distributed sources; Applications of large-scale data analytics; Foundations of cluster computing and parallel data processing; Introduction of selected relevant frameworks (e.g., Apache Hadoop and Spark). MapReduce; Advanced programming concepts for large-scale data analytics; Concepts and tools for large-scale data storage; Stream data analytics; Event Processing; Techniques and open-source tools for large-scale analytics; Computational statistics and machine learning with large-scale data processing frameworks such as Spark. Columnar data storage.
(Language of instruction: English)
Learning Outcomes
- Be able to define large-scale data analytics and understand its characteristics
- Be able to explain and apply concepts and tools for distributed and parallel processing of large-scale data
- Know how to explain and apply concepts and tools for highly scalable storage, querying, filtering, sorting and synthesizing of data
- Know how to describe and apply selected statistical and machine learning techniques and tools for the analysis of large-scale data
- Know how to explain and apply approaches to stream data analytics and event processing
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Learning Spark: Lightning-Fast Big Data Analytics." by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia
Publisher: O'Reilly - "Hadoop: The Definitive Guide" by Tom White
ISBN: 9781449311520.
Publisher: "O'Reilly Media, Inc." - "Large-Scale Data Analytics" by Aris Gkoulalas-Divanis,Abderrahim Labbi
ISBN: 1461492424.
Publisher: Springer Science & Business Media
Note: Module offerings and details may be subject to change.
OptionalCT5134: Agents, Multi-Agent Systems and Reinforcement Learning
CT5134: Agents, Multi-Agent Systems and Reinforcement Learning
Semester 2 | Credits: 5
The topic of Agents and Multi-Agent Systems, examines environments that involve autonomous decision making software actors to interact with their surroundings with the aim of achieving some individual or overall goal. A typical agent environment could be a trading environment where an agent attempts to optimise energy usage, or the profitability of a transaction. More recently, significant global attention has focused on the vision of autonomous vehicles, which also follows the core principle of an agent attempting to achieve a set of defined goals.
This module begins by examining the underpinnings of what is an Agent, and how we can better understand the principles of an agent and its autonomy. Multi-Agent Systems are then explored, as a means of understanding how many agents can interact with each other in a complex environment. Agents are commonly modelled using Game Theory, and in this module a range of Game Theoretic Models will be studied.
The module will also examine Adaptive Learning Agents through the use of Reinforcement Learning, which focuses on training learners to choose actions which yield the maximum reward in the absence of prior knowledge. The module takes a hands-on, practical approach to reinforcement learning theory, beginning with Markov Decision Processes, detailing practical learning examples and how to formulate a reinforcement learning task.
(Language of instruction: English)
Learning Outcomes
- Explain and discuss the principles underlying Agents.
- Explain the role of game theory and games in agent design.
- Apply the principle of agents to a range of simulation problems.
- Formulate a decision making problem as a Markov decision process (MDP)
- Apply reinforcement learning algorithms to learn policies for MDPs
- Conduct experiments to determine appropriate hyperparameters for a reinforcement learning algorithm
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5134: "Agents, Multi-Agent Systems and Reinforcement Learning " and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
OptionalCT5121: Advanced Topics in Natural Language Processing
CT5121: Advanced Topics in Natural Language Processing
Semester 2 | Credits: 5
Advanced topics in natural language processing, including deep learning for NLP, machine translation and language resources. This module covers topics in the following areas:
* Use of neural networks, deep learning and large language models for solving NLP tasks
* Advanced NLP techniques including textual similarity, event extraction and question answering.
* Multilingual and mulitmodal NLP techniques including machine translation
* Applications of NLP in digital humanities, legal NLP, language learning or other similar areas
(Language of instruction: English)
Learning Outcomes
- Use deep learning, neural networks and large language models for NLP
- Synthesize practical knowledge of NLP to complex tasks in data analytics.
- Apply multilingual NLP and build simple machine translation systems.
- Create complex solutions for real world problems using NLP technologies
- Solve novel NLP challenges using deep learning technologies
Assessments
- Written Assessment (50%)
- Continuous Assessment (50%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
The above information outlines module CT5121: "Advanced Topics in Natural Language Processing" and is valid from 2024 onwards.Note: Module offerings and details may be subject to change.
OptionalCT5113: Web and Network Science
CT5113: Web and Network Science
Semester 2 | Credits: 5
Web and Network Science is concerned with techniques and technologies for analysing, modelling and learning from data that is represented as a graph. The module takes a practical approach to the analysis and modelling of large network data sets from multiple domains. Students will learn the fundamentals of graph theory and how to apply graph analysis, modelling and evaluation techniques to real data sets for applications such as recommendation, authority ranking, link-prediction and community detection. The practical work in this module is done using the R programming language - and learners are expected to be able to programme in R.
(Language of instruction: English)
Learning Outcomes
- Explain and discuss the theoretical principles behind graph analysis and modelling
- pre-process, load and analyse data in a variety of network formats and standards
- measure the fundamental properties of a graph
- apply fundamentals of comparative network modelling to real data sets
- apply the principles and techniques of graph partitioning, community-finding and modularity analysis to real network data
- apply the core techniques in social network analysis to analyse a real social graph
- apply graph analytical techniques to applications such as recommender systems, user role analysis and link prediction
- visualise network data, where appropriate
Assessments
- Written Assessment (65%)
- Continuous Assessment (35%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Networks" by Mark Newman
Publisher: Oxford - "Statistical Analysis of Network Data with R" by Eric D. D. Kolaczyk ; Gábor Csárdi
ISBN: 978-149390982.
Publisher: Springer
Note: Module offerings and details may be subject to change.
OptionalCT5187: Knowledge Representation
CT5187: Knowledge Representation
Semester 2 | Credits: 5
This module introduces students to Knowledge Representation (KR) and reasoning using formal logic.
Planned topics include: Foundations of knowledge representation. Propositional and first-order logic (FOL). Foundations of reasoning. Logic programming. Satisfiability Solving (SAT) and Answer Set Programming (ASP). Probabilistic logics and uncertainty reasoning. Basics of machine learning in the context of KR.
(Language of instruction: English)
Learning Outcomes
- Explain the fundamental principles of knowledge representation and reasoning
- Describe and use syntax and semantics of important formal logics
- Explain and be able to use fundamental types of reasoning and logic frameworks
- Model application domains using logic languages and relational knowledge representation formats
- Explain and apply selected Machine Learning models in the context of knowledge representation and reasoning
Assessments
- Written Assessment (70%)
- Continuous Assessment (30%)
Teachers & Administrators
Click a name to search for their researcher profile. Note: Only teachers publish research profiles.
Reading List
- "Knowledge Representation and Reasoning" by Ronald J. Brachman, Hector J. Levesque
Publisher: Elsevier/Morgan Kaufmann
Note: Module offerings and details may be subject to change.
- Proactive Cybersecurity Systems: Learn to build algorithms and systems that can proactively defend organisation against cyber threats.
- Industry connections: Learn from the Local and International (EU/Abroad) cybersecurity industry during talks and case studies in modules.
- Master core concepts: Develop a solid foundation in cybersecurity theory and practical, applying them to real-world scenarios.
- Enhance analytical skills: Build the ability to interpret and analyse data using advanced tools and methodologies.
- Develop professional expertise: Hone the skills required to succeed in diverse roles, including AI, data analytics, and cybersecurity.
- Improve communication skills: Learn to effectively present and articulate findings to a range of audiences, from stakeholders to decision-makers.
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 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.
How will I learn?
The MSc in Adaptive Cybersecurity blends innovative teaching with hands-on learning for a well-rounded educational experience. You'll engage in interactive lectures, flipped classrooms, seminars, and workshops led by expert faculty. Real-world case studies, data-driven projects, and coding exercises help bridge theory and practice.
Collaborative projects build teamwork and communication skills, while individual assignments and a final capstone project with your dedicated supervisor foster independence and critical thinking.
You'll have access to cutting-edge tools, industry-standard software, high performance computing, and real-world datasets to support your learning and career development.
Further to that, ACSPeerLear Club, where you can learn from your peers.
How Will I Be Assessed?
Throughout the programme, your progress is assessed through various coursework and exams, including reports, essays, presentations, and computer assignments.
- Continuous Assessment - Regular coursework, including essays, presentations and in-class tests. Students receive regular (weekly) feedback on their progress. (around 30%).
- Examinations - Written exams take place before Christmas and in April-May. Written and oral exams evaluate proficiency in theoretical and practical understanding, grammar, vocabulary, comprehension, and communication.
- Project Work - Research and subtitling projects and translation assignments allow students to apply their skills in real-world contexts.
Course queries:
MScCS-ACS@universityofgalway.ie
Programme Director(s):
Dr Ihsan Ullah,
School of Computer Science,
College of Science & Engineering
E: MScCS-ACS@universityofgalway.ie
T: +353 91 493 836
Graduates of the MSc in AI will be able to:
- Strong Programming & Mathematical Foundation: Proficiency in programming languages and a solid grasp of linear algebra, calculus, probability, and statistics are fundamental for understanding and implementing AI algorithms.
- Data Handling and Analytical Skills: The ability to clean, transform, analyse, and visualise data is crucial, along with a keen problem-solving and critical thinking mindset to dissect complex AI challenges.
- Research and Independent Learning: Given the rapidly evolving nature of AI, skills in information retrieval, academic research methods, and a strong capacity for continuous self-directed learning are vital for success in the program's research components and beyond.
- Effective Communication: Clearly articulating complex technical concepts, research findings, and project outcomes through both written reports and verbal presentations is essential for academic collaboration and future career progression.
- Teamwork and Ethical Awareness: The ability to collaborate effectively on projects and a strong understanding of the ethical implications and societal impact of AI are increasingly important skills for responsible AI development. Apply enhanced critical thinking and analytical skills to their object of study.
- Plan, manage, and execute a substantial independent study project
- Reflect deeply on a range of research perspectives, topics, and approaches related to the object of study.
- Exhibit the ability to self-assess and self-direct.
Accreditations & Awards
Meet our Employers
Entry Requirements and Fees
Minimum Entry Requirements
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 Entry Requirements
Overall, entry to the MSc Adaptive Cybersecurity 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.
More information on English language test equivalency are available here.
Supporting Documents
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.
You can apply online to the University of Galway application portal here.
Please review the entry requirements set out in the section above.
You will be required to upload supporting documentation to your application electronically. See the section above on entry requirements for further information on the supporting documentation required for this course.
Closing Dates
For this programme, there is no specific closing date for receipt of applications. Applications will be accepted on a rolling basis and course quotes will be reviewed continuously throughout the application cycle.
Notes
- You will need an active email account to use the website and you'll be guided through the system, step by step, until you complete the online form.
- Browse the FAQ's section for further guidance.
Fees for Academic Year 2025/2026
Course Type | Year | EU Tuition | Student Contribution | Non-EU Tuition | Levy | Total Fee | Total EU Fee | Total Non-EU Fee |
---|---|---|---|---|---|---|---|---|
Masters Full Time | 1 | €8,750 | €28,000 | €140 | €8,890 | €28,140 |
For 25/26 entrants, where the course duration is greater than 1 year, there is an inflationary increase approved of 3.4% per annum for continuing years fees.
Postgraduate students in receipt of a SUSI grant – please note an F4 grant is where SUSI will pay €4,000 towards your tuition (2025/26). You will be liable for the remainder of the total fee. A P1 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.
Postgraduate Excellence Scholarships
This scholarship is valued at €1,500 for EU students applying for full-time taught master's postgraduate courses. You will be eligible if:
- You have been accepted to a full-time taught master's course at University of Galway,
- You have attained a first class honours (or equivalent) in a Level 8 primary degree.
An application for the scholarship scheme is required (separate to the application for a place on the programme). The application portal for 2025 is now open and available here. Applications will close on the 30th September 2025. Full details available here.
Excellence and Merit Scholarships
We offer three Excellence Scholarships worth €10,000 each as well as €2,000 Merit Scholarships for students applying from outside the EU in each of the following programmes:
- MSc Data Analytics
- MSc Artificial Intelligence
- MSc Adaptive Cybersecurity
- MSc Software Design and Development
Application Process
Students applying for full time postgraduate programmes from outside of the European Union (EU), You can apply online to the University of Galway application portal here.
Our application portal opens on the 1st October each year for entry the following September.
Further Information
Please visit the postgraduate admissions webpage for further information on closing dates, documentation requirements, application fees and the application process.
Why University of Galway?
World renowned research led university nestled in the vibrant heart of Galway city on Ireland's scenic West Coast.
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Course Introduction
Emerging area of AI-driven and data analytics-driven proactive cybersecurity
This 12-month full-time programme provides cutting edge technical training and research opportunities in the field 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 experts based in the School of Computer Science.
