【Author】
McGinn, Dan; Birch, David; Akroyd, David; Molina-Solana, Miguel; Guo, Yike; Knottenbelt, William J.
【Abstract】This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.
【Keywords】big data analytics; bitcoin; cryptocurrency; large-scale graph visualization; money laundering; pattern recognition; structured data
【摘要】这项工作提出了一个系统的自顶向下的比特币交易活动可视化，以探索动态生成的算法行为模式。比特币主导着加密货币市场，为研究人员提供了丰富的实时交易数据来源。数据的假名但公开的性质为发现人类和算法行为模式提供了机会，这对许多方面(如金融监管机构、协议设计人员和安全分析师)都感兴趣。然而，保持底层数据的视觉保真度，以对网络内的活动保持更全面的理解，仍然具有挑战性，尤其是在实时情况下。我们展示了一种有效的力导向图可视化，用于我们的大规模数据观察设施，以加速数据探索，并在领域专家和普通公众中获得有用的见解。本文演示的高保真可视化允许协作发现意外的高频交易模式，包括自动洗钱操作，以及对比特币网络的多种不同算法拒绝服务攻击的演变。
【关键词】大数据分析;比特币;加密货币;大规模图可视化;洗钱;模式识别;结构化数据
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