Late Submission

No extensions will be granted without an approved application for Special Consideration. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late. For example, 25 hours late in submission for an assignment worth 10 marks – 20% penalty or 2 marks deducted from the total.  No submission will be accepted after solutions have been posted.

Supplementary Exam

If you receive special consideration for the final exam, a supplementary exam will be scheduled after the normal exam period, following the release of marks. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.

Classes

There will be one two-hour lecture each week and one one-hour workshop, 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. 

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. 

Unit Schedule

The indicative list of topics is shown here, this is subject to change based on feedback from the class.