【Author】
Li, Ji; Gu, Chunxiang; Wei, Fushan; Chen, Xi
【Source】BLOCKCHAIN AND TRUSTWORTHY SYSTEMS, BLOCKSYS 2019
【Abstract】With the more and more extensive application of blockchain, blockchain security has been widely concerned by the society and deeply studied by scholars, of which anomaly detection is an important problem. Data mining techniques, including conventional machine learning, deep learning and graph learning, have been concentrated for anomaly detection in the last few years. This paper presents a systematic survey of the blockchain anomaly detection results using data mining techniques. The anomaly detection methods are classified into 2 main categories, namely universal detection methods and specific detection methods, which contain 8 subclasses. For each subclass, the corresponding research are listed and compared, presenting a systematic and categorized overview of the current perspectives for blockchain anomaly detection. In addition, this paper contributes in discussing the advantages and disadvantages for the data mining techniques employed, and suggesting future directions for anomaly detection methods. This survey helps researchers to have a general comprehension of the anomaly detection field and its application in blockchain data.
【Keywords】Blockchain; Anomaly detection; Data mining; Graph analysis; Network security
【摘要】随着区块链的应用越来越广泛，区块链安全受到了社会的广泛关注和学者的深入研究，其中异常检测是一个重要问题。数据挖掘技术，包括传统的机器学习、深度学习和图学习，在过去的几年里被集中用于异常检测。本文对应用数据挖掘技术的区块链异常检测结果进行了系统的综述。异常检测方法主要分为两大类，即通用检测方法和特定检测方法，其中包含8个子类。对每个子类的相关研究进行了列举和比较，对当前区块链异常检测的研究前景进行了系统、分类的概述。此外，本文还讨论了所采用的数据挖掘技术的优缺点，并对异常检测方法的未来发展方向提出了建议。这一调查有助于研究人员对异常检测领域及其在区块链数据中的应用有一个大致的认识。
【关键词】区块链;异常检测;数据挖掘;图分析;网络安全
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