Cybercrime researchers at the University of Adelaide and San Jose State University have enlisted Oracle Autonomous Database to host the world’s first Biometric Analyser and Network Extractor (BANE) platform.
Created to better combat transnational organised crime and child exploitation, the BANE platform leverages the scalability and security of Oracle Autonomous Database running on Oracle Cloud Infrastructure (OCI) to help law enforcement automate and catalogue the rapidly growing amount of digital evidence such as pictures, videos, digital communications, posts and other content.
The proliferation of child sexual abuse material (CSAM) is rapidly outpacing law enforcement’s ability to address the problem. Automated software tools using biometric systems help investigations but are limited due to a reliance on a single biometric cue, such as the face.
In an effort to augment investigative practices, Associate Professors Russell Brewer and Bryce Westlake, have created a software platform using Oracle Autonomous Database to extract faces and voices from CSAM seized by police, to rapidly identify offenders and victims, as well as the connections between them.
“When it comes to organised crime and child exploitation, there’s just too much data for law enforcement to realistically be able to look at everything,” said Bryce Westlake, associate professor in the Department of Justice Studies at San Jose State University. “If investigators seize a computer or a couple of iPads, some phones, and a dozen USB sticks, they may have 500,000 files or more that they need to analyse. BANE is a powerful new platform that will enable law enforcement agencies to much more quickly consolidate and automate data review, enabling faster investigations compared to manual methods.”
When done manually, seizing, cataloguing, analysing, and storing CSAM at scale can be laborious, time consuming, and psychologically detrimental to investigators.
With the BANE platform, investigators can process thousands of hours of sensitive content in a matter of hours, enabling investigations to move faster and more efficiently while also minimising the mental load on investigators.
“This technology has the potential to prevent kids from harm by allowing police to do more and undertake investigations at scale, not only more rapidly, but also more effectively,” said Russell Brewer, associate professor of Criminology at the University of Adelaide. “This enables us to think beyond an individual police jurisdiction, bringing together data across the country and across the world. The more data you can throw at a case, the more effective you are going to be in terms of identifying offenders and victims.”
Oracle Autonomous Database was selected for its automated management and the scalability and performance of OCI. It features a data pipeline that uses AI and machine learning to scan digital evidence – whether videos, still images, emails, or bank transactions – to extract and match similarities in data, including face and voice biometrics and store the results in a database for subsequent review by investigators.
This real-time approach to investigations made possible by the new OCI-powered platform offers many advantages over previous techniques by providing an improved ability to draw new links between offenders and their associations with specific victims.
This can, in turn, improve investigatory outcomes, such as helping to keep a child out of danger or helping arrest an offender more rapidly.
“With Oracle Autonomous Database, we can make things faster, easier, less resource intensive and more secure. And at the same time, we’re reducing costs as well. Using OCI, there’s no need to invest in on-premises infrastructure such as servers and attendant costs. This makes it easier to deploy the solution and scale it up as needed,” Brewer added.
BANE Can Address Private Sector Security and Cybercrime
Transnational serious and organised crime (TSOC) currently costs the Australian economy approximately AUD $60 billion and is growing.
While BANE was designed specifically to assist in the investigation of child exploitation and protection cases, the goal of the researchers is to eventually collect, catalogue, and analyse various forms of criminal data and evidence for predictive intelligence across law enforcement domains.
This same model can also be extended and reused in the private sector as well, such as banking and finance or supply chain security.
Through controlled testing with law enforcement agencies, the University-led project has demonstrated how Autonomous Database on OCI can be used to help address the exploding amount of digital evidence and the growing issue of cybercrime.
At the same time, the project optimises faster and more cost-efficient workflows to combat cybercrime more effectively while also minimising overall exposure of sensitive content to investigators and intelligence analysts.
