FIT3183 - Malicious AI and dark side security

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Overview

In this unit you will learn first-hand some of the required skills of a Chief Security Officer. You will be exposed to the latest technologies deployed by attackers against AI and security, and mechanisms to counter these malicious activities. This unit gives you the opportunity to engage in role-playing case … For more content click the Read More button below.

In this unit you will learn first-hand some of the required skills of a Chief Security Officer. You will be exposed to the latest technologies deployed by attackers against AI and security, and mechanisms to counter these malicious activities. This unit gives you the opportunity to engage in role-playing case studies wherein you will be required to participate against other students in a security warfare: security vs anti-security technologies, such as, deepfakes vs anti-deepfakes, forensics vs counter forensics.

Offerings

S2-01-MALAYSIA-ON-CAMPUS

Requisites

Prerequisite

Contacts

Chief Examiner(s)

Professor Raphael Phan

Notes

IMPORTANT NOTICE:
Scheduled teaching activities and/or workload information are subject to change in response to COVID-19, please check your Unit timetable and Unit Moodle site for more details.

Learning outcomes

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

identify and evaluate malicious technologies deployed by attackers against AI and security such as adversarial machine learning, deepfakes, ransomware, kleptography, cryptovirology, covert exfiltration, anti-forensics, subversion.

implement counter-anti-security technologies to combat the anti-security techniques deployed by attackers.

design and deploy security or anti-security techniques in real-world situations.

critically assess the level of security offered by existing AI and security systems.

explain the ethical principles between the dark side and cybersecurity for social good.

Assessment

1 - In-semester assessment

2 - Examination (2 hours and 10 minutes)

Scheduled and non-scheduled teaching activities

Laboratories

Lectures

Workload requirements

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.