• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

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

Original Article

Securing Smart Contracts: Harnessing the Power of Efficient NetB2 Detection

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


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)


  1. Abdelaziz T, Hobor A. Smart Learning to Find Dumb Contracts. 2023. Available from: https://doi.org/10.48550/arXiv.2304.10726
  2. Alaba FA, Sulaimon HA, Marisa MI, Najeem O. Smart Contracts Security Application and Challenges: A Review. Cloud Computing and Data Science. 2023;5(1):15–41. Available from: https://dx.doi.org/10.37256/ccds.5120233271
  3. Khan SN, Loukil F, Ghedira-Guegan C, Benkhelifa E, Bani-Hani A. Blockchain smart contracts: Applications, challenges, and future trends. Peer-to-Peer Networking and Applications. 2021;14(5):2901–2925. Available from: https://dx.doi.org/10.1007/s12083-021-01127-0
  4. He D, Deng Z, Zhang Y, Chan S, Cheng Y, Guizani N. Smart Contract Vulnerability Analysis and Security Audit. IEEE Network. 2020;34(5):276–282. Available from: https://dx.doi.org/10.1109/mnet.001.1900656
  5. Zhuang Y, Liu Z, Qian P, Liu Q, Wang X, He Q. Smart Contract Vulnerability Detection using Graph Neural Network. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. (pp. 3283-3290) International Joint Conferences on Artificial Intelligence Organization. 2020.
  6. Liu Z, Qian P, Wang X, Zhuang Y, Qiu L, Wang X. Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection. IEEE Transactions on Knowledge and Data Engineering. 2023;35(2):1296 –1310. Available from: https://dx.doi.org/10.1109/tkde.2021.3095196
  7. Chen C, Su J, Chen J, Wang Y, Bi T, Wang Y, et al. When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We? 2023. Available from: https://doi.org/10.48550/arXiv.2309.05520
  8. Liu Z, Jiang M, Zhang S, Zhang J, Liu Y. A Smart Contract Vulnerability Detection Mechanism Based on Deep Learning and Expert Rules. IEEE Access. 2023;11:77990–77999. Available from: https://dx.doi.org/10.1109/access.2023.3298048
  9. Sun X, Tu L, Zhang J, Cai J, Li B, Wang Y. ASSBert: Active and semi-supervised bert for smart contract vulnerability detection. Journal of Information Security and Applications. 2023;73:103423. Available from: https://dx.doi.org/10.1016/j.jisa.2023.103423
  10. Jie W, Chen Q, Wang J, Koe ASV, Li J, Huang P, et al. A novel extended multimodal AI framework towards vulnerability detection in smart contracts. Information Sciences. 2023;636:118907. Available from: https://dx.doi.org/10.1016/j.ins.2023.03.132
  11. Cai J, Li B, Zhang J, Sun X, Chen B. Combine sliced joint graph with graph neural networks for smart contract vulnerability detection. Journal of Systems and Software. 2023;195:111550. Available from: https://dx.doi.org/10.1016/j.jss.2022.111550
  12. Mangrulkar RS, Chavan PV. Ethereum Blockchain. In: Blockchain Essentials. (pp. 123-166) Berkeley, CA, USA. Apress. 2024.
  13. Gohil MR, Maduskar SS, Gajria V, Mangrulkar R. Blockchain and Its Applications in Healthcare. In: Enabling Blockchain Technology for Secure Networking and Communications. (pp. 271-294) IGI Global. 2021.


© 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)


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