Different types of malicious activities have been flagged in multiple permissionless blockchains such as bitcoin, Ethereum etc. While some malicious activities exploit vulnerabilities in the infrastructure of the blockchain, some target its users through social engineering techniques. To address these problems, we aim at automatically flagging blockchain accounts that originate such malicious exploitation of accounts of other participants. To that end, we identify a robust supervised machine learning (ML) algorithm that is resistant to any bias induced by an over representation of certain malicious activity in the available dataset, as well as is robust against adversarial attacks. We find that most of the malicious activities reported thus far, for example, in Ethereum blockchain ecosystem, behaves statistically similar. Further, the previously used ML algorithms for identifying malicious accounts show bias towards a particular malicious activity which is over-represented. In the sequel, we identify that Neural Networks (NN) holds up the best in the face of such bias inducing dataset at the same time being robust against certain adversarial attacks.

翻译：尽管一些恶意活动利用了该链中基础设施的弱点,但有些则通过社会工程技术针对其用户。为了解决这些问题,我们的目标是自动标出源于此类恶意利用其他参与者账户的链条账户。为此,我们确定一种强有力的、有监督的机器学习算法,这种算法能够抵御现有数据集中某些恶意活动过度表现引起的任何偏见,并且能够有力地抵御对抗性攻击。我们发现,迄今为止所报告的大多数恶意活动在统计上类似,例如Ethereum链生态系统中。此外,先前用于识别恶意账户的ML算法显示,对某一特定恶意活动存在偏向性,而这种恶意活动代表过多。在后续数据中,我们确定Neural网络(NN)在面对这种偏见时,在吸引数据时保持最佳的优势。