Ethereum smart contract exploitation detection using machine learning
Vulnerabilities in Ethereum smart contracts may be exploited by malicious actors for financial gains. While many vulnerability detection tools are available, these tools are not perfect and vulnerable smart contracts may still be deployed into the Ethereum blockchain. As such, the detection and i...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/162852 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Vulnerabilities in Ethereum smart contracts may be exploited by malicious actors for financial
gains. While many vulnerability detection tools are available, these tools are not perfect and
vulnerable smart contracts may still be deployed into the Ethereum blockchain. As such, the
detection and identification of malicious transactions becomes important for contract owners
and the community.
In this project, we propose the use of anomaly detection machine learning algorithms to detect
malicious transactions based on information recorded on the blockchain. Malicious
transactions are considered anomalies and are generally uncommon as compared to benign
transactions. By grouping existing smart contracts of similar functionalities, we can build a
machine learning model using historical transactions information from these smart contracts
and apply it to detect future malicious transactions. We will also evaluate the effectiveness of
our approach on past exploitations in the Ethereum main net and present the results. |
|---|