A Visualization Assisted Abnormal Trading Detection System for Multi-crypto Currencies
PI: Prof. SM Yiu, HKU
Co-I: Prof. TW Lam, HKU
Up to now, there are thousands of cryptocurrencies exist in the market, including bitcoin, Ether. But cryptocurrencies impose a critical threat to existing monetary systems due to its anonymity in nature, for examples, money laundry and making profits by disseminating rumors. In this project, we aim at designing and implementing a system to assist the analysis of detecting abnormal transactions in crypto-currencies. This system has two objectives. The first one is to allow investigators to visualize the problematic transaction clusters in a user-friendly manner. There is no existing free tools that allow a user to identify these problematic transaction clusters easily. The second objective is to allow investigators (e.g. investigators from law-enforcement units and/or commercial financial institutes) to receive warnings from our systems. That is, we hope to automatically identify some abnormal transaction clusters and provide warnings to the investigators. Technically, our system integrates data collection, data analysis, and data visualization to provide stakeholders (including exchanges and investors) with effective information about crypto-currencies so as to help them make rational decisions (in addition to the crime case investigation of law enforcement units).