BitcoinHeist: Topological Data Analysis for Ransomware Prediction on the Bitcoin Blockchain
BitcoinHeist: Topological Data Analysis for Ransomware Prediction on the Bitcoin Blockchain
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DOI：
10.24963/ijcai.2020/612
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
C. Akcora;Yitao Li;Y. Gel;Murat Kantarcioglu
中科院分区：
文献类型：
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作者：
C. Akcora;Yitao Li;Y. Gel;Murat Kantarcioglu
最近允许伪匿名交易的加密货币的激增导致了各种电子犯罪活动的激增，特别是通过加密敏感用户数据来要求赎金的黑客攻击中的加密货币支付。目前，大多数黑客使用比特币进行支付，现有的勒索软件检测工具仅依赖于几个加密和/或繁琐的数据收集步骤。通过利用拓扑数据分析的最新进展，我们提出了一种新的高效且易于处理的框架，可以自动预测勒索软件家族中的新勒索软件交易，仅考虑过去交易的有限记录。此外，我们的新方法在检测新勒索软件家族的出现方面具有很高的实用性，即检测没有过去交易记录的勒索软件。
Recent proliferation of cryptocurrencies that allow for pseudo-anonymous transactions has resulted in a spike of various e-crime activities and, particularly, cryptocurrency payments in hacking attacks demanding ransom by encrypting sensitive user data. Currently, most hackers use Bitcoin for payments, and existing ransomware detection tools depend only on a couple of heuristics and/or tedious data gathering steps. By capitalizing on the recent advances in Topological Data Analysis, we propose a novel efficient and tractable framework to automatically predict new ransomware transactions in a ransomware family, given only limited records of past transactions. Moreover, our new methodology exhibits high utility to detect emergence of new ransomware families, that is, detecting ransomware with no past records of transactions.