National College of Ireland
MSc in Cyber Security - FULL TIME
- MSCCYB1 A
"Release Date: Sunday 04 Feb, 2025"
"Due Date (Proposal), 21 Feb 2025 23:59 hrs"
"Due Date (Project), 25 April 2025 23:59 hrs"
"Lecturers: Noel Cosgrave, Liam Mccabe, Abdul Shahid"
______________________________________________________________________
"H9AIMLC - AI/ML in Cybersecurity"
Continuous Assessment (CA) Type: Project
Weight: The assignment will be marked out of 100. This CA is worth 100% of the overall marks for
this module.
Instructions: "Individual Project"
SUBMISSION DETAILS:
The document must be submitted as a Word or PDF document to Moodle before the deadline. Include
student name, student ID, and course name at the top of the first page.
Late submissions will not be penalized if the student applied for an extension through NCI360 and it was
approved.
TURNITIN: All report submissions will be electronically screened for evidence of academic misconduct
(i.e., plagiarism and collusion)
Duration of Continuous Assessment:
Project Proposal: The deadline to submit a project proposal is Week-5 (25 Feb 2024 23:59 hrs)
Final Project submission: The final project report submission is Week-13 (21 April 2024 23:59 hrs)
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1. Module Learning Outcomes
On successful completion of this module the learner will be able to:
LO1
LO2
LO3
LO4
LO1
Critically analyse AI and machine learning techniques to assess best practice guidance and
ethical implications when applied to specific cybersecurity problems.
Extract, clean and transform datasets in preparation for machine learning, and build evaluate
machine learning models to extract knowledge from various cybersecurity datasets.
Critically review current AI and machine learning research and assess ethical considerations
and research methods applied in the field.
Evaluate and utilise AI and machine learning technologies when designing and
implementing cybersecurity solutions.
Critically analyse AI and machine learning techniques to assess best practice guidance and
ethical implications when applied to specific cybersecurity problems.
2. Project Overview
Produce a report that describes a proposal for applying Artificial Intelligence and Machine Learning
Technique to solve a problem in Cybersecurity area.
The proposal for the project should include a literature review (minimum of 7 research papers). Explain
the background and context of your investigation and identify as clearly as possible the exact topic and/or
hypotheses you propose to investigate. In the literature review, you should judge material from the
literature that you will have read to assist in developing your proposal. When reading papers, technical
reports etc., for the literature review for your assignment, try to analyse: how the paper is structured,
including details of how the ideas are developed; the writer’s decisions of what material to include or
exclude in their paper, which material is covered in detail, which is mentioned only briefly and which is
referred to in references; how they build arguments to justify the work that they report in the paper; and
the style of language and choice of vocabulary. You may also find it helpful to study these features from
other papers that you encounter while working on this assignment since different writers approach these
issues in different ways and can make different choices when structuring and writing their papers.
In developing your project, you should seek to identify a question or questions relevant to AI and machine
learning which need to be directly answered in the existing literature. You should then develop a plan for
investigating this question. This plan may typically include identifying techniques that you want to
improve, which may enable a set of repeatable experiments to be carried out. The plan should include
exploring the use of an existing publicly available data set to support your proposed investigation. The
plan should include integrating machine learning techniques into a naive system to make it intelligent. It
would be best if you also considered how to investigate the research question, how you will be
applying/integrating algorithms or machine learning algorithms to your model so that it can make
intelligent decisions, what needs to be measured and what metrics will be used, and how the results will
be analysed.
In your document, you must explain how you want to develop a solution to the problem (of the chosen
topic of interest) you have identified from your investigation. You should produce an application design.
The application could be a data-driven model built using machine learning techniques and tools or
advanced AI algorithms that can be applied to an existing problem to make that more efficient and/or
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intelligent. You should compare your models/methods/applications with existing (state-of-the-art)
methods/models/applications.
In summary, students should aim at performing the following tasks: (i) identifying a problem, (ii) identifying
baseline system(s) from the literature, (iii) proposing a new solution, (iv) explaining how outcomes of your
models are to be assessed.
Projects will be assessed based on their novelty, technical quality, insightfulness, depth, clarity, and
quality of writing. Algorithms and resources used in a report should be described completely.
3. Key details, Requirements, and Definitions:
Deliverable: There are two deliverables for this project, Project Proposal and Project Report:
The project proposal should consider the following points:
I.
II.
III.
IV.
V.
Proposal Report (PDF format) NOTE: Ensure that your name in full (as per NCI
official documents) and student number are visible on the front page (template is
uploaded on the Moodle Page).
Project name, which shows clear identification of domain knowledge.
Project description, including introduction, related work (Justification).
The project proposal must include state-of-the-art related work (minimum seven
research papers).
The project proposal must be 2-3 pages long, expect references.
The project proposal must show unambiguously evidence of the following:
1. A Critical analysis of data analytics approaches and machine learning methods to
assess best practice guidance when applied to Cybersecurity problems.
2. The proposal may include a description of datasets to be used in preparation for
building data-driven models (e.g., machine learning methods).
3. The proposal should include what evaluation methods to use to measure the
performance of the Machine learning methods/models/applications and the importance
of advance data analytics methods.
4. The critical review of relevant machine learning techniques/research to afford the
assessment of methods to be applied in the project.
4. Final Project Submission:
The details about the final report are listed from this section and onward. The Final submission is
expected to be prepared in research paper format having at least these following sections.
Title: The title should properly reflect the selected topic.
Abstract: 100–150 words providing a high-level description of the project and the domain of the datasets
used.
Keywords: 4-5 keywords explain the overall domain knowledge
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Introduction: It should motivate the work, present, and discuss the research question(s) / objective(s) of
the project and (optionally) provide a concise overview of the following sections.
Related Work: This should not only summarize the related works or existing methods but also critically
evaluate their key positive and negative aspects with respect to the topic and domain of the project, i.e.,
how well/badly do the related works artefact address your question(s) / objective(s), what aspects are
useful to consider, what are the limitations, etc. If you’ve reused a method already applied to this dataset,
discuss what you expect to gain by doing this.
Methodology and Implementation: This section can be named differently. But it should describe how
your approach answers the research questions. Additional (technical) details can also be discussed here.
You should also include here a discussion on key preliminary aspects of the methodology, such as how
the datasets were used in the study and different steps for preparing the dataset. Essentially, you should
recount how you performed your experimentation (e.g. ML models) to address your problem or research
question(s).
Evaluation: What performance measures have you used for evaluation and why (discuss how the choice
of performance measures is appropriate)?
Conclusions and Future Work: Summarize your proposal and discuss the limitations of your proposed
methods. Summarize how your proposed methods will answer the research question(s) at a high level
and note the key implications of your project with respect to the methods proposed.
References: Include a list of all references used in your report
5. Marking Grid
Total project weighting: 100% of the final mark. The project of this coursework will be graded using the
marking grid shown in the Table. Note that marks in the Table represent percentages.
Grading Criteria:







Objectives and Motivation
Discussion and Related Work.
Choice of Methods
Methodology and Implementation
Evaluation
Conclusion and Future Work
Quality
6. AI policy
1. You may use generative AI to enhance learning, research, and ideation processes.
2. Always cite AI outputs and prompts. Proper citations must be included. Failure to do
so is in violation of academic integrity policies.
3. Don’t trust, always verify. Always cross-check AI outputs with credible sources to
ensure output accuracy and relevance.
4. Think critically. Question assumptions, perspectives, and potential biases presented in
the AI-generated outputs. Beware of AI hallucinations.
5. Learn, experiment and practice. Experiment with different AI tools, prompts and use
cases. Try incorporating AI across your research, presentations, and papers.
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6. Use AI ethically and responsibly. If used responsibly, generative AI tools can enhance
teaching and learning. However, if plagiarism is suspected, per existing policy,
lecturers reserve the right to verify learning outcome achievement through alternative
assessment.
7. Administrative Data






CRITERIA
High H1 (80-100)
Objectives
and
Motivation
(5%)
Discussion
and Related
Work (25%)
Choice
Methods
(15%)
The assignment must be electronically submitted via Turnitin on Moodle. Please note
that email submissions will not be accepted.
No printed copy is required, please do not print.
Please note that the lecturer cannot grant extensions. Extensions can only be
granted if approved after the submission of a personal circumstances form. Please
consult NCI360 or other support services for further information and to file a form if
required.
In case of unjustified late submissions, marks will be deducted based on standard
School of Computer Science policy.
All coursework will be electronically evaluated (via Turnitin) for evidence of academic
misconduct including plagiarism.
Please ensure that all work submitted is your own and that you follow correct
referencing practices.
of
Methodology
(30%)
Very
challenging
project objectives are
well presented, met
and
thoroughly
motivated as well as
discussed.
Discussion of related
work is excellent, and
the choice of papers to
discuss
excellently
situates the project
within the literature
The student has
studied a selection of
complex
methods
illustrating a well
thought out approach
to addressing their
objective(s).
It is hard to find fault in
the approach.
H1 (70-79)
H2.1 (60-69)
H2.2 (50-59)
Pass (40-49)
Challenging
project
objectives are well
presented, met and
thoroughly motivated as
well as discussed.
Appropriate
project
objectives are well
presented, met and
thoroughly motivated
as well as discussed.
Appropriate
project
objectives are presented,
mostly met and motivated
as well as discussed.
There are clear
objectives, which
are at least partially
met.
Discussion of related
work is very good, and
the choice of papers to
discuss
excellently
situates the project
within the literature.
Discussion of related
work is good and the
choice of papers to
discuss well situates
the project within the
literature.
Discussion of related work
is appropriate and the
choice of papers to
discuss well situates the
project
within
the
literature.
Discussion
of
related work is
appropriate, and
the choice of
papers
appropriately
situates the project
within the literature.
The student has studied
a
selection
some
complex
methods
illustrating a well thought
out
approach
to
addressing
their
objective(s).
All stages of proposed
approach are rigorously
covered.
Application of at least
two
advanced
methods.
Application of at least one
advanced method.
The student has
appropriately
selected methods
to address their
objective(s)
but
played it safe.
All stages of proposed
approach
are
rigorously
covered.
Some minor shortcuts
or errors may be
present.
All stages of proposed
approach
are
appropriately covered, but
the general approach
lacks some depth. There
may be some mistakes in
the approach taken.
All
stages
of
proposed approach
are appropriately
covered, but the
general approach
lacks depth. There
may be significant
mistakes in the
proposed
approach.
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Fail (marks
Cannot discern
project objectives,
and/or if project
objectives were met.
Discussion of related
work lacks depth, or
the choice of papers
seems somewhat
arbitrary.
Choice of methods
appears arbitrary, or
not well justified.
methods not
appropriately
covered. The
approach proposed
may also be hard to
discern.
Evaluation
(10%)
Conclusion
and Future
Work (10%)
Quality. (5%)
The student aims at
investigating a diverse
range of situations to
give a very rich
understanding
of
performance. All key
decisions are justified
with
appropriate
literature
The student aims at
investigating a diverse
range of situations to
give
a
rich
understanding
of
performance. All key
decisions are justified
with
appropriate
literature.
Insightful conclusions,
which appreciate key
limitations
and
implications of the
project.
Key
implications of the
project are anchored
with relevant literature.
Well-conceived and
thought-out
future
work is discussed.
Insightful conclusions,
which
appreciate
limitations
and
implications of the
project. Implications of
the project are anchored
with relevant literature.
Well-conceived
and
thought-out future work
is discussed.
Exceptionally
well
written, and presented,
with no mistakes in
formatting
or
referencing. Report
provided in PDF
format.
Well written, with no
major language errors.
All figures are well
conceived and readable.
The
HARVARD
template is adhered to.
Report does not exceed
the
length
limits.
References
are
appropriately
and
correctly used. Report
provided in PDF format.
The student aims at
investigating a number
of situations to give a
better understanding
of performance. Most
of the key decisions
are justified with
appropriate literature.
Implications
and
limitations
well
understood.
Discussion
also
correctly highlights key
takeaways.
Appropriate
future
work is discussed and
presented.
Main document has a
few language or style
errors. The figures are
well
presented.
HARVARD template
and length limit are
adhered
to.
References
are
complete,
and
correctly used. Report
provided in PDF
format.
Page 6 of 6
The student aims at
investigating
some
situations to give a better
understanding
of
performance. Some of the
Key decisions are justified
with appropriate literature.
More depth of
proposed
differentiated
evaluation
is
necessary
to
provide a better
understanding of
performance.
Some
key
decisions
are
justified
with
appropriate
literature
Implications
and
limitations
well
understood. Discussion
also correctly highlights
key takeaways. Future
work lacks depth and
creativity
but
is
appropriate.
Implications and
limitations not well
understood. Future
work lacks depth
and creativity but is
appropriate.
Main
document
is
readable with some
language or style errors.
Some figures are mostly
well
presented.
HARVARD template is
largely
adhered
to.
References are mostly
complete and correctly
used. Report not in PDF
format.
Main document is
readable with some
language or style
errors.
Some
figures may be hard
to
read
or
presented in a
suboptimal
manner.
HARVARD
template is largely
adhered
to.
References
are
mostly complete
and correctly used.
Report not in PDF
format.
Key decisions are not
justified
or
substantiated with
appropriate
literature. The project
may also lack depth
or complexity in
several key aspects
Implications
and
limitations
not
understood. Future
work seems arbitrary
or inconsistent with
project findings.
Littered with typos, or
poor use of English.
HARVARD template
may have been
broken. Figures may
be hard to read.
References (if any)
are
probably
incomplete. Report
not in PDF format.