  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.
