A Griffith University-led project into detecting criminal cryptocurrency payments has been awarded nearly $600,000 in funding to address security issues that cause Australians to lose millions of dollars each year.
The Office of National Intelligence and the Department of Defence National Security Science and Technology Centre are funding the National Intelligence and Security Discovery Research Grants (NISDRG) program, which was awarded to Associate Professor Vallipuram Muthukkumarasamy from School of Information and Communication Technology and his project partners Professor Ryan Ko from the University of Queensland and Professor Marimuthu Palaniswami from the University of Melbourne.
“Cryptocurrency comes with anonymity or pseudo anonymity, so law enforcement authorities find it very difficult to link certain payments with illegal activity,” Associate Professor Muthukkumarasamy said.
“As it is not central bank issued currency, it is not controlled by any organisation or any country.
“This project will create new tools based on artificial intelligence to analyse anonymised transactions and link them with financial crime patterns to effectively identify perpetrators.
“It will develop a novel investigative toolkit to facilitate attribution – such as source identification and legal evidence – of criminals linked to digital payments to crime in almost real time.”
Associate Professor Muthukkumarasmy said there were no algorithms or techniques to unravel the hidden connections from a massive network of digital payments, which is being misused by criminals.
“Therefore, the proposed big data analytics will exploit the hidden connections and reveal the linkages to automatically reconstruct the provenance – information flows and payment histories of criminal activities – in near real time.”
There are more than 2000 unregulated cryptocurrencies worldwide. These massive digital networks and frequency with which anonymous transactions occur pose numerous challenges to the existing financial systems’ stability and the ability of Law Enforcement Agencies (LEAs) to intelligently identify and link these transactions to crimes.
With the advent of cryptocurrencies, criminals can hide their identities and launder money by transferring their illicit collection into digital wallets.
These transactions can be tracked as they are based on public ledgers and there is a traceable link for each transaction. However, criminals can then use services such as ‘mixing’ or ‘tumbling’ to convert ‘dirty money’ into ‘clean money’.
The services also break a transaction into smaller amounts, which are then sent to clusters of new addresses in the network repeatedly, and finally arriving at the ‘clean wallet’. This process removes the link from ‘dirty wallet’ to the ‘clean wallet’ and therefore, cannot establish the link to the ‘root wallet’.
The money that is now in the ‘clean wallet’ can be used for legitimate purposes.
Associate Professor Muthukkumarasamy pioneered the Network Security teaching and research at Griffith University and is leading the Networking & Security and Blockchain Research Group at the Institute for Integrated and Intelligent Systems. He also led the development of the first Master of Cyber Security program in Queensland.
The aims of the project include:
- Design and develop an efficient multi-source feature extraction mechanism to facilitate gathering intelligent information and security alerts about suspicious cryptocurrency payments;
- Design and build a high usability automated provenance reconstruction tool to facilitate forensic investigators for real time attribution of payments to crime.
Minister for Defence the Hon Peter Dutton MP announced Griffith as among the eight Australian universities to receive funding for projects in the first round of the NISDRG program.
“The targeted funding for Australia’s higher-education sector will enhance our ability to deal with threats to our national interests through the development of cutting-edge technology,” Minister Dutton said.
The three-year project ‘Linking Digital Payments to Crime Using Big Data Machine Learning Tools’ received $590,843 in funding.