Online quizzes, in-class activities, or scheduled tests and exam must be undertaken at the time indicated in the unit guide. Should these activities be missed due to illness or misadventure, students may apply for Special Consideration.

All other assessments must be submitted by 11:55 pm on their due date.

Late Assessment Submission Penalty

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7^th day (including weekends). After the 7^th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern.

For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, please apply for Special Consideration.

Assessments where Late Submissions will be accepted 

Class participation -- YES, Standard Late Penalty applies

Assignment, Group project and presentation, and Final examination -- NO, unless Speical Consideration is Granted

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through ask.mq.edu.au.

Class participation

Assessment Type ¹: Participatory task Indicative Time on Task ²: 0 hours Due: Weekly Weighting: 10%

Each week, a mark will be awarded based on the level of participation shown by students in the discussion during the lectures.

On successful completion you will be able to:

• Explain the basic concepts and the limitations of Artificial Intelligence.
• Detect intrusion in networks and systems by applying tools and techniques revealing abnormal patterns in datasets.
• Communicate professionally in written and oral form to a range of audiences.
• Analyse the trends of applications of Artificial Intelligence in cyber security.

Final examination

Assessment Type ¹: Examination Indicative Time on Task ²: 15 hours Due: Exam Week Weighting: 45%

A three hour examination in the exam period.

On successful completion you will be able to:

• Explain the basic concepts and the limitations of Artificial Intelligence.
• Communicate professionally in written and oral form to a range of audiences.
• Analyse the trends of applications of Artificial Intelligence in cyber security.

Assignment

Assessment Type ¹: Project Indicative Time on Task ²: 30 hours Due: Week 7 Weighting: 25%

In this assignment, the student will be given a series of datasets and will be asked to develop an analysis of this data and provide a report. The aim of this task is to be able to identify unusual patterns and abnormal activity using data.

On successful completion you will be able to:

• Detect intrusion in networks and systems by applying tools and techniques revealing abnormal patterns in datasets.
• Communicate professionally in written and oral form to a range of audiences.

Group project and presentation

Assessment Type ¹: Project Indicative Time on Task ²: 30 hours Due: Week 12 Weighting: 20%

In this assessment task, students as a group will be required to research and evaluate a tool leveraging AI for cyber security purposes. The task also involves a presentation of the findings.

On successful completion you will be able to:

• Detect intrusion in networks and systems by applying tools and techniques revealing abnormal patterns in datasets.
• Communicate professionally in written and oral form to a range of audiences.
• Analyse the trends of applications of Artificial Intelligence in cyber security.

¹ If you need help with your assignment, please contact:

• the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
• the Writing Centre for academic skills support.

² Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Requirements to Pass this Unit

To pass this unit you must:

• Achieve a total mark equal to or greater than 50%, and

• Participate in, and undertake all hurdle activities for, a minimum of 9 of the 12 weekly workshops, and

• Achieve at least 50% in the final examination

There will be one two-hour lecture each week and one one-hour workshop (starting from Week 1), you can find the time and location information can be found via MQ Timetables. You are expected to attend both classes as they provide complimentary learning activities each week. In practical classes you will write code and do experiments, and in lectures we will mainly discuss the theories, principles and methods.

Methods of Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to convenors can either be placed on the iLearn discussion board or sent to their email address from your university email address.

Textbooks

We do not have a single specific textbook, but will refer to the following texts for your reference during the semester:

• David Freeman, Clarence Chio, "Machine Learning and Security", O'Reilly Media, Inc., 2018. (electronic edition available via MQ Library)
• Sumeet Dua, Xian Du, "Data Mining and Machine Learning in Cybersecurity", Auerbach Publications, 2011.
• Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita, "Network Anomaly Detection: A Machine Learning Perspective", Chapman and Hall/CRC, 2013.

You will be given readings from these and other sources each week.

Technology Used and Required

We will make use of Python 3 for the analysis of cyber security related datasets, including a range of modules such as scikit-learn, pandas, numpy, tensorflow, etc. that provide additional features. These can all be installed via the Anaconda Python distribution. We will discuss this environment and the installation process in the first week of classes.

Project Work

A major part of the assessment in this unit is based on a project that you will complete in group. This will allow you to explore the techniques you are learning from classes in a real-world exercise of applying machine learning in cybersecurity.

COVID Information

For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.