Detecting and Predicting Financial Darknet Activities

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A wide range of negative activities such as misinformation, spamming messages, hack attacks, online frauds, etc. can originate from financial darknet services which are typically paid for in cryptocurrencies. Although the technical community has made efforts to understand how some of the financial darknet activists operate, there is a lack of comprehensive framework for monitoring financial darknet contents and associating them to cryptocurrency transactions for detecting potential negative activities.

In this project, we will build a data collection capability on dubious/negative activities from both open social media and hidden darknet such as TOR networks. We will then use computational social science methods, combining social science theory of deviation behaviour and machine learning/knowledge graph algorithms, to mine the collected darknet data and derive predictive models of darknet events/issues.

We will then build a real-time data collection capability on cryptocurrency transactions and use machine learning algorithms to detect suspicious transactions and associate them with darknet events/issues. Finally, we will produce and commercialize an interactive risk activity monitoring system, based on the resulting predictive models coupled with updated financial darknet data and cryptocurrency transactions.

This financial darknet and cryptocurrency monitoring system will potentially serve a variety of institutional clients, including regulators, investment firms, consulting firms, think tanks, academic research units, news media, NGOs, etc. The system can be commercialized in two formats: syndicated products such as daily or weekly trends reports on a subscription-fee basis, and customized services such as live-monitoring visualizers tailored made for specific clients. We will launch the products and services in Hong Kong and gradually expand to other parts of the world.

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