School of Engineering, Information Technology and Physical Sciences

Leveraging distributed machine learning for Threat Intelligence in Blockchain networks

Project Title:

Leveraging distributed machine learning for Threat Intelligence in Blockchain networks

Supervisors:

Dr Iqbal Gondal (SoEITPS), Dr Taiwo Oseni (SoEITPS)

Contact Person:

Dr Iqbal Gondal Iqbal.gondal@federation.edu.au

Project Brief

Threat intelligence, or cyber threat intelligence, is information an organisation uses to understand the threats that have, will, or are currently targeting the organisation. This information is used to prepare, prevent, and identify cyber threats looking to take advantage of valuable resources. A blockchain as a trustworthy and secure decentralised and distributed network has been emerged for many applications such as in banking, finance, insurance, healthcare and business. Recently, many communities in blockchain networks want to deploy machine learning models to get insights on the information gathered through threat intelligence from geographically distributed large-scale data owned by each participant. To run a learning model without data centralisation, distributed machine learning (DML) for blockchain networks has been studied. While several works have been proposed, privacy and security have not been sufficiently addressed, and there are vulnerabilities in the architecture and limitations in terms of efficiency. The aim of this project is to propose a privacy-preserving DML model for a blockchain to obtain valuable insights using threat intelligence information to prepare, prevent, and identify cyber threats in a systematic way an organisation may face.