COMP SCI 7101B - Cyber Security Research Project Part B
North Terrace Campus - Trimester 1 - 2024
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General Course Information
Course Details
Course Code COMP SCI 7101B Course Cyber Security Research Project Part B Coordinating Unit Computer Science Term Trimester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 12 Contact Up to 2 hours per week Available for Study Abroad and Exchange N Prerequisites COMP SCI 7101A Assumed Knowledge COMP SCI 7308 Restrictions Only available to students in the Master of Cyber Security Assessment Milestone presentations and reports Course Staff
Course Coordinator: Dr Sherif Haggag
Course Timetable
The full timetable of all activities for this course can be accessed from Course Planner.
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Learning Outcomes
Course Learning Outcomes
Upon completing this course, you will be able to:â¯â¯
use appropriate and relevant methodologies for data processing and analysis
apply industry standard data analysis techniques to develop algorithms and synthesise, apply, and evaluate new workflows and presentation of data
derive conclusions and solutions relevant to your chosen research question and real-world context, engaging with data science technical, management, and strategic considerations
present and report your research and results to stakeholders.University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Attribute 1: Deep discipline knowledge and intellectual breadth
Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.
1 Attribute 3: Teamwork and communication skills
Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.
2,3 Attribute 7: Digital capabilities
Graduates are well prepared for living, learning and working in a digital society.
3,4 Attribute 8: Self-awareness and emotional intelligence
Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.
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Learning Resources
Required Resources
Course readings list
You can find the full course readings list, and access those readings here Links to an external site.. The readings are also available via the 'course readings' link in the left navigation pane.
Independent reading and research
As you progress through the course and the development of your own individual research project, it is expected that you will conduct substantial independent reading of academic and grey literature.
Your supervisor can assist you with this, and you can find extra guidance and useful resources around finding and evaluating literature for research in module 4.Recommended Resources
Non-compulsory readings
This is a project-based course so there are no prescribed compulsory readings: you'll be engaging extensively with literature related to your topic as you progress your project.
There are some useful texts and resources that you may wish to reference throughout your project, and they are linked here for your reference.
Geron, A 2019, Hands-on machine learning with Scikit-learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems, O’Reilly Media, Incorporated. Links to an external site.
Chollet, F 2021, Deep learning with Python, 2nd edn, Manning Publications Co. Links to an external site.
McCallum, Q. E. 2012, Bad Data Handbook, O’Reilly Media, Incorporated. Links to an external site.
Brownlee, J 2020, Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python, Machine Learning Mastery. Links to an external site.
Pajankar, A 2021 Practical Python data visualization : a fast track approach to learning data visualization with Python, Apress. Links to an external site.
Milovanović, I, Foures, D & Vettigli, G 2015, Python data visualization cookbook, 2nd edn, Packt Publishing, Birmingham. Links to an external site.
Pandian, S 2021, Effective data visualization techniques in data science using Python. Analytics Vidhya, viewed 8 February 2022. Links to an external site.
Conferences
Online Learning
The following conference publications may be useful:
IEEE International Conferences on Data Science and Advanced Analytics
International Conferences on Data Science, Technology and Applications Links to an external site.
Relevant conferences can be found at the Data Science Conferences Index. ( Links to an external site.The quality of conferences can be judged using the CORE Conference Portal) Note: while A* is the best ranking, you will note that many of the conferences listed in the first link are not ranked, so there are limitations.
Key project and data references
You will be referencing the GovHack Links to an external site. and Data.gov.au Links to an external site. websites extensively throughout this course.
Wherever possible, resources that are open-access or available through the University of Adelaide Library have been provided.
In some cases, additional resources have been flagged that may not be provided through the Library. These are not required textbooks.
Depending on the specific needs of your research project, and your own professional interest, you may wish to consider purchasing access to them individually for your own reference, though the course does not require this of you.
If you are struggling to find or access relevant reference materials and resources to support you with components of your research, you should consult with your Supervisor, who can help you find appropriate alternatives.
Additional recommended and supplementary readings are also provided within individual modules and sections throughout the course as relevant. -
Learning & Teaching Activities
Learning & Teaching Modes
You should meet weekly with your supervisorWorkload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
You need to work at least 20 hours per week on this courseLearning Activities Summary
Upon completing this course, you will be able to:
use appropriate and relevant methodologies for data processing and analysis
apply industry standard data analysis techniques to develop algorithms and synthesise, apply, and evaluate new workflows and presentation of data
derive conclusions and solutions relevant to your chosen research question and real-world context, engaging with data science technical, management, and strategic considerations
present and report your research and results to stakeholders.Specific Course Requirements
no specific requirements for this course -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
midterm report: 30%
Presentation: 20%
Final report: 50%Assessment Related Requirements
You will need to submit 3 submissions for 3 marked assignmentsAssessment Detail
Research Project B follows the structure of Research Project A: instead of studying prescribed course content and receiving support from tutors, you will continue to develop your own research project with the support of a supervisor who will provide on-going guidance and feedback. While some resources have been provided on this course site to assist you as you progress through your course and approach the assessments, these are not the core learning resources for the course: the bulk of your learning will occur as you engage with the literature on your research topic and progress your research project.Submission
All submission dates are listed on myuniCourse Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
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- Student Experience of Learning and Teaching Policy
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