Students

COMP8325 – Applications of Artificial Intelligence for Cyber Security

2022 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Muhammad Ikram
muhammad.ikram@mq.edu.au
Xuyun Zhang
xuyun.zhang@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
(COMP6320 or ITEC653) or admission to MInfoTechCyberSec
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description
This unit deals with the applications of Artificial Intelligence in the field of Cyber Security. Topics covered include machine learning-based intrusion detection systems, malware detection, AI as a service, digital forensics, incident response leveraging SIEM data. Special attention will be given to the concept of adversarial machine learning.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Explain the basic concepts and the limitations of Artificial Intelligence.
  • ULO2: Detect intrusion in networks and systems by applying tools and techniques revealing abnormal patterns in datasets.
  • ULO3: Communicate professionally in written and oral form to a range of audiences.
  • ULO4: Analyse the trends of applications of Artificial Intelligence in cyber security.

General Assessment Information

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 9:00 pm on their due date.

Late Submissions

Late submissions will be accepted but will incur a penalty unless there is an approved Special Consideration request. A 12-hour grace period will be given after which the following deductions will be applied to the awarded assessment mark: 12 to 24 hours late = 10% deduction; for each day thereafter, an additional 10% per day or part thereof will be applied until five days beyond the due date. After this time, a mark of zero (0) will be given. For example, an assessment worth 20% is due 5 pm on 1 January. Student A submits the assessment at 1 pm, 3 January. The assessment received a mark of 15/20. A 20% deduction is then applied to the mark of 15, resulting in the loss of three (3) marks. Student A is then awarded a final mark of 12/20.

Assessment Tasks

Name Weighting Hurdle Due
Class participation 10% No Weekly
Final examination 45% No Exam Week
Assignment 25% No Week 7
Group project and presentation 20% No Week 12

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

Delivery and Resources

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

  Week Topic
          1 Course overview; Python basics
          2 Overview of ML application in cyber security
          3 Regression and classification
          4 Anomaly detection I
          5 Anomaly detection II
          6 Private and secure machine learning
          7 Behaviour metrics attacks (recorded due to public holiday)
           8 Vulnerability and malware analysis (recorded due to public holiday)
           9 Botnets, DDoS attacks, and network traffic analysis
          10 Spam emails and phishing URLs
          11 Digital forensics and incident response
          12 Guest lecture
          13 Revision

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via AskMQ, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Unit information based on version 2022.02 of the Handbook