Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Janhavi Satam; Sangeeta Vhatkar

doi:10.17485/IJST/v17i15.617

Article

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i15.617

Year: 2024, Volume: 17, Issue: 15, Pages: 1621-1632

Original Article

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

Janhavi Satam^1*, Sangeeta Vhatkar¹

¹Department of Information Technology, Thakur College of Engineering and Technology, Mumbai, 400101, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:01 March 2024, Accepted Date:21 March 2024, Published Date:15 April 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: Using a variety of datasets from the Ethereum documentation and Smart Contract Dataset repository, this study tackles the crucial problem of classifying smart contract vulnerabilities. Methods: Our study uses a three-module method and focuses on the Resource 3 Dataset, which contains over 2,000 Ethereum smart contracts, including inherited contracts. The groundwork for deep learning model training is laid in Module 1 by extracting bytecode from Solidity files and creating images thereafter. In Colab, Module 2 entails importing data, pre-processing, SMOTE balancing, and building three deep learning models: CNN, XCEPTION, and EfficientNet-B2. Module 3 is a Flask-based web application created in Visual Studio Code that enables vulnerability predictions, bytecode extraction, and user interaction. Findings: With an overall accuracy of 71 percent, the Convolutional Neural Network (CNN) displays its effectiveness in classifying vulnerabilities. Although the accuracy of XCEPTION and EfficientNet-B2 is 69% and 75%, respectively, the latter is the top performer. Novelty & Applications: The online application adds to the comprehensive examination of smart contract security by giving users an easy-to-use interface. The EfficientNet-B2 model stands out as a dependable tool for precise vulnerability classification, and this study advances our understanding of and efforts to mitigate vulnerabilities in Ethereum smart contracts.

Keywords: Smart Contracts, Vulnerability Classification, Ethereum, Deep Learning, Convolutional Neural Network (CNN)

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Copyright

© 2024 Satam & Vhatkar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)