Cryptocurrency is a form of digital or virtual currency that utilizes robust cryptographic techniques to ensure security and decentralized transactions. Unlike traditional currencies, the transaction records of cryptocurrencies are stored in a distributed ledger based on blockchain technology, and anyone wishing to record a transaction in the system must abide by a consensus protocol. As a result, cryptocurrencies possess characteristics such as decentralization, security, anonymity, and global accessibility.

While anyone can access the blockchain to view cryptocurrency transactions, there are significant technical barriers, such as parsing blockchain data and efficiently storing data. Meeting the analytical demands of specific scenarios efficiently remains a challenging task. This project, drawing upon extensive research and industry experience, aims to construct a cryptocurrency tracking system capable of reconstructing the entire cryptocurrency circulation process. Based on this system, we offer solutions to the market that include industry information, data analysis, smart contract development, and decision support.

Furthermore, due to the characteristics of cryptocurrency, such as anonymity, resistance to regulation, and cross-border mobility, cryptocurrencies are often used for payments in financial dark web services. Although the technical community has made efforts to understand financial darknet operations, there needs to be a comprehensive framework for monitoring financial darknet contents and associating them with cryptocurrency transactions to detect potential negative activities. We will establish data collection capabilities to gather suspicious/negative activity data from both public social media and hidden dark web sources like the TOR network. Subsequently, we will employ computational social science methods, incorporating social science theories on deviant behavior and machine learning/knowledge graph algorithms, to mine the collected dark web data and derive predictive models for dark web events/issues. Then, we will integrate this with the aforementioned cryptocurrency tracking system and use machine learning algorithms to detect suspicious transactions, correlating them with dark web events/issues. Finally, based on the predictive models and updated financial dark web data and cryptocurrency transactions, we will develop and commercialize an interactive risk activity monitoring system.