镜子背后的恶魔：追踪暗网上加密货币滥用活动的运作 campaign
The Devil Behind the Mirror: Tracking the Campaigns of Cryptocurrency Abuses on the Dark Web
AI总结：
本文通过采集4,923个加密货币相关 onion 站点并分析非法区块链交易，系统追踪暗网加密货币滥用，识别2,564个非法站点、1,189个地址、90.8 BTC收入及66个 campaign。
摘要：
暗网已成为增强匿名性的最先进解决方案。正如一把双刃剑，它也在不经意间成为非法活动的安全网和滋生地。其中，加密货币被普遍滥用于在规避监管的同时收取非法收入。尽管打击非法活动的努力持续不断，但对于暗网上加密货币滥用的特征和动态，仍缺乏深入理解。在本研究中，我们开展了一项多维且系统的研究，以追踪暗网上与加密货币相关的非法活动及其运作 campaign。我们首先采集了一个包含4,923个与加密货币相关的 onion 站点、超过130K个页面的数据集。随后，我们检测并提取非法区块链交易，以刻画加密货币滥用的特征，重点关注来自单一/成簇地址以及非法 campaign 的特征。在整个研究过程中，我们识别出2,564个非法站点，涉及1,189个非法区块链地址，这些地址对应的收入达90.8 BTC。基于它们之间的内在联系，我们进一步识别出其背后的66个 campaign。我们的探索表明，暗网上的非法活动具有很强的关联性，这可以指导我们识别新的非法区块链地址和 onion 站点，并在其部署的早期阶段发出警报。
英文摘要：
The dark web has emerged as the state-of-the-art solution for enhanced anonymity. Just like a double-edged sword, it also inadvertently becomes the safety net and breeding ground for illicit activities. Among them, cryptocurrencies have been prevalently abused to receive illicit income while evading regulations. Despite the continuing efforts to combat illicit activities, there is still a lack of an in-depth understanding regarding the characteristics and dynamics of cryptocurrency abuses on the dark web. In this work, we conduct a multi-dimensional and systematic study to track cryptocurrency-related illicit activities and campaigns on the dark web. We first harvest a dataset of 4,923 cryptocurrency-related onion sites with over 130K pages. Then, we detect and extract the illicit blockchain transactions to characterize the cryptocurrency abuses, targeting features from single/clustered addresses and illicit campaigns. Throughout our study, we have identified 2,564 illicit sites with 1,189 illicit blockchain addresses, which account for 90.8 BTC in revenue. Based on their inner connections, we further identify 66 campaigns behind them. Our exploration suggests that illicit activities on the dark web have strong correlations, which can guide us to identify new illicit blockchain addresses and onions, and raise alarms at the early stage of their deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
