Vol.15 Issue 2 (2019)
Artificial Intelligence Enabled Cyber Fraud: A Detailed Look into Payment Diversion Fraud and Ransomware
Alana Maurushat, Abubakar Bello & Braxton Bragg
Cyber fraud is rampant. The recent Covid 19 pandemic is a good example of the same. Domain Tools in April 2020 identified over 65,000 websites have been identified as fraud scams related to Covid-19. Organisations have lost billions of money in online scams, and in particular with payment diversion fraud (‘PDF’) and ransomware. PDF is a type of cyber-attack where an entity is tricked into making a direct payment from its account to a false supplier/entity often using real-time payment methods. Ransomware is a type of malicious software that prevents users from accessing their system or personal files usually by locking them through encryption, and demands ransom payment in order to regain access. Based on the professional experience of the authors, coupled with current literature, there is a growing trend of automation, with the use of machine-learning and artificial intelligence. This article discusses PDF and ransomware in the context of mechanics and emerging trends for systematic attacks and response by private industry. These case studies illustrate the limited role that the law plays in the investigation and response to cyber fraud.
Alana Maurushat is Professor of Cyber security and Behaviour at Western Sydney University and a Board Director with the cybercrime investigations firms IFW Global. ** Dr. Abubakar Bello is Lecturer in Cyber security and Behaviour with a strong industry background in information security, risk management and digital forensics. He is also an expert in Nigerian cybercrime. Both are researchers with the Socially Engineered Payment Diversion Fraud (SocEngPDF) project. *** Braxton Bragg is a Research Assistant with SocEngPDF; he is a US-licensed attorney completing a Masters in Cyber security and previously held legal and accounting roles with private US firms. Thank you to student intern Kevin Tang for his research.