COMP SCI 7101B - Cyber Security Research Project Part B

North Terrace Campus - Trimester 1 - 2024

This is an advanced research project conducted individually under the guidance of an academic supervisor. It gives students the chance to investigate and contribute to an area at the cutting edge of cyber security. As part of the project students will present their work to an audience and write a major report detailing their results. Project topics vary from year to year depending on staff availability and research focus.

  • 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.

  • Learning Outcomes
    Course Learning Outcomes
    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.
    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.


    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.


    Attribute 7: Digital capabilities

    Graduates are well prepared for living, learning and working in a digital society.


    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.

  • 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.

    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 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 supervisor

    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 course
    Learning 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:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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 assignments
    Assessment 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.
    All submission dates are listed on myuni
    Course 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.

  • 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 ( 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.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

The University of Adelaide is committed to regular reviews of the courses and programs it offers to students. The University of Adelaide therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.