# Vulnerability and Transaction behavior based detection of Malicious Smart Contracts

arXiv: https://arxiv.org/abs/2106.13422 · HTML: https://mlsanity.com/paper/2106.13422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts](https://mlsanity.com/paper/1910.10601/) (2019)
- [Detecting Malicious Accounts in Permissionless Blockchains using Temporal Graph Properties](https://mlsanity.com/paper/2007.05169/) (2020)
- [Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains](https://mlsanity.com/paper/2101.11915/) (2021)
- [Empirical Vulnerability Analysis of Automated Smart Contracts Security Testing on Blockchains](https://mlsanity.com/paper/1809.02702/) (2018)
- [A Data Science Approach for Honeypot Detection in Ethereum](https://mlsanity.com/paper/1910.01449/) (2019)

## Abstract (arXiv)

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.