使用时间图属性检测非许可区块链中的恶意账户
Detecting Malicious Accounts in Permissionless Blockchains using Temporal Graph Properties
摘要：
将账户建模为节点，将交易建模为有向图中的有向边（对于区块链）的时间特性使我们能够理解账户的行为（恶意或良性）。将帐户预测分类为恶意或良性可以帮助无许可区块链平台的用户以安全的方式操作。出于这一动机，我们引入了时间特性，如突发和吸引力的几个已经使用的图形属性，如节点度和聚类系数。使用识别的特征，我们训练各种机器学习（ML）算法，并确定在检测哪些帐户是恶意的方面表现最好的算法。然后，我们在为帐户分配恶意标签之前，研究帐户在数据集不同时间粒度上的行为。对于以太坊区块链，我们确定对于整个数据集-ExtraTreesClassifier在监督ML算法中表现最好。另一方面，在整个数据集上使用无监督ML算法（如K-Means）提供的结果的基础上使用余弦相似性，我们能够检测到554个可疑账户。此外，使用帐户的行为变化分析，我们确定了814个独特的可疑帐户在不同的时间粒度。
英文摘要：
The temporal nature of modeling accounts as nodes and transactions as directed edges in a directed graph -- for a blockchain, enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning (ML) algorithms and identify the algorithm that performs the best in detecting which accounts are malicious. We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For Ethereum blockchain, we identify that for the entire dataset - the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect 554 more suspicious accounts. Further, using behavior change analysis for accounts, we identify 814 unique suspicious accounts across different temporal granularities.
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。
