  Smart Contracts (SCs) in Ethereum can automate tasks and provide different
functionalities to a user. Such automation is enabled by the `Turing-complete'
nature of the programming language (Solidity) in which SCs are written. This
also opens up different vulnerabilities and bugs in SCs that malicious actors
exploit to carry out malicious or illegal activities on the cryptocurrency
platform. In this work, we study the correlation between malicious activities
and the vulnerabilities present in SCs and find that some malicious activities
are correlated with certain types of vulnerabilities. We then develop and study
the feasibility of a scoring mechanism that corresponds to the severity of the
vulnerabilities present in SCs to determine if it is a relevant feature to
identify suspicious SCs. We analyze the utility of severity score towards
detection of suspicious SCs using unsupervised machine learning (ML) algorithms
across different temporal granularities and identify behavioral changes. In our
experiments with on-chain SCs, we were able to find a total of 1094 benign SCs
across different granularities which behave similar to malicious SCs, with the
inclusion of the smart contract vulnerability scores in the feature set.

Cryptography and Security;Distributed, Parallel, and Cluster Computing;Machine Learning