Vulnerability and Transaction behavior based detection of Malicious Smart Contracts
This work addresses the problem of detecting malicious smart contracts for blockchain security, but it is incremental as it builds on existing vulnerability analysis and machine learning approaches.
The study investigated the correlation between vulnerabilities in Ethereum smart contracts and malicious activities, finding that certain vulnerabilities are linked to specific malicious behaviors. By incorporating vulnerability severity scores into an unsupervised machine learning model, the researchers identified 1,094 benign smart contracts that exhibited behavior similar to malicious ones across different temporal granularities.
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.