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Original Article

An Enhanced Deep Learning Framework for Smart Contract Vulnerability Detection in Ethereum Blockchain

Chikkala V V S R Harshadeep1 Dr. K. V. Ramana2
1 PG Scholar, Department of Computer Science and Engineering, University College of Engineering Kakinada, JNTUK, Kakinada, Andhra Pradesh, India. 2 Senior Professor, Department of Computer Science and Engineering, University College of Engineering Kakinada, JNTUK, Kakinada, Andhra Pradesh, India.

Published Online: July-August 2026

Pages: 180-192

References

1. J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language
understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
2. NCC Group, “Decentralized Application Security Project (DASP) Top 10,” 2018. [Online]. Available: https://dasp.co/
3. J. F. Ferreira, P. Cruz, T. Durieux and R. Abreu, “SmartBugs: A framework to analyze Solidity smart contracts,” in Proc. 35th
IEEE/ACM Int. Conf. on Automated Software Engineering (ASE), 2020, pp. 1349–1352.
4. C. S. Yashavant, M. Chavda, S. Kumar, A. Karkare and A. Karmakar, “SCRUBD: Smart contracts reentrancy and unhandled
exceptions vulnerability dataset,” in Proc. IEEE/ACM 22nd Int. Conf. on Mining Software Repositories (MSR), Data and Tool
Showcase Track, 2025, pp. 149–153.
5. Z. Zheng, J. Su, J. Chen, D. Lo, Z. Zhong and M. Ye, “DAppSCAN: Building large-scale datasets for smart contract weaknesses in
DApp projects,” IEEE Trans. Software Engineering, vol. 50, no. 6, 2024.
6. T. Hu, J. Li, B. Li and A. Storhaug, “Why smart contracts reported as vulnerable were not exploited?,” IEEE Trans. Dependable and
Secure Computing, vol. 22, no. 3, pp. 2579–2596, 2025.
7. S. J. Alsunaidi, H. Aljamaan and M. Hammoudeh, “DIVE: A multi-label smart contract vulnerability dataset,” Scientific Data, vol. 13,
no. 1, art. 664, 2026.
8. S. J. Alsunaidi, H. Aljamaan and M. Hammoudeh, “MultiTagging: A vulnerable smart contract labeling and evaluation framework,”
Electronics, vol. 13, no. 23, art. 4616, 2024.
9. Z. Feng, D. Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, D. Jiang and M. Zhou, “CodeBERT: A pre-trained
model for programming and natural languages,” in Findings of the ACL: EMNLP, 2020, pp. 1536–1547.
10. D. Guo, S. Ren, S. Lu, Z. Feng, D. Tang, S. Liu, L. Zhou, N. Duan et al., “GraphCodeBERT: Pre-training code representations with
data flow,” in Proc. 9th Int. Conf. on Learning Representations (ICLR), 2021.
11. Y. Huang, J. Li, S. Fang, Y. Li, P. Yang, B. Hu and T. Zhang, “Smart contract intent detection with pre-trained programming language
model,” arXiv:2508.20086, 2025, doi: 10.48550/arXiv.2508.20086.
12. L. Luu, D.-H. Chu, H. Olickel, P. Saxena and A. Hobor, “Making smart contracts smarter,” in Proc. ACM SIGSAC Conf. on Computer
and Communications Security (CCS), 2016, pp. 254–269.13. J. Feist, G. Grieco and A. Groce, “Slither: A static analysis framework for smart contracts,” in Proc. IEEE/ACM 2nd Int. Workshop
on Emerging Trends in Software Engineering for Blockchain (WETSEB), 2019, pp. 8–15.
14. Y. Zhuang, Z. Liu, P. Qian, Q. Liu, X. Wang and Q. He, “Smart contract vulnerability detection using graph neural network,” in Proc.
29th Int. Joint Conf. on Artificial Intelligence (IJCAI), 2020, pp. 3283–3290.
15. H. Wu, Z. Zhang, S. Wang, Y. Lei, B. Lin, Y. Qin, H. Zhang and X. Mao, “Peculiar: Smart contract vulnerability detection based on
crucial data flow graph and pre-training techniques,” in Proc. IEEE 32nd Int. Symp. on Software Reliability Engineering (ISSRE),
2021, pp. 378–389.
16. F. Luo, R. Luo, T. Chen, A. Qiao, Z. He, S. Song, Y. Jiang and S. Li, “SCVHunter: Smart contract vulnerability detection based on
heterogeneous graph attention network,” in Proc. IEEE/ACM 46th Int. Conf. on Software Engineering (ICSE), 2024.
17. L. S. H. Colin, P. M. Mohan, J. Pan and P. L. K. Keong, “An integrated smart contract vulnerability detection tool using multi-layer
perceptron on real-time Solidity smart contracts,” IEEE Access, vol. 12, pp. 23549–23567, 2024.
18. H. J. Kang, K. L. Aw and D. Lo, “Detecting false alarms from automatic static analysis tools: How far are we?,” in Proc. IEEE/ACM
44th Int. Conf. on Software Engineering (ICSE), 2022, pp. 698–709.
19. M. Allamanis, “The adverse effects of code duplication in machine learning models of code,” in Proc. ACM SIGPLAN Int. Symp. on
New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!), 2019, pp. 143–153.
20. R. Croft, M. A. Babar and M. M. Kholoosi, “Data quality for software vulnerability datasets,” in Proc. IEEE/ACM 45th Int. Conf. on
Software Engineering (ICSE), 2023, pp. 121–133.
21. Y. Ding, Y. Fu, O. Ibrahim, C. Sitawarin, X. Chen, B. Alomair, D. Wagner, B. Ray and Y. Chen, “Vulnerability detection with code
language models: How far are we?,” in Proc. IEEE/ACM 47th Int. Conf. on Software Engineering (ICSE), 2025.
22. D. Perez and B. Livshits, “Smart contract vulnerabilities: Vulnerable does not imply exploited,” in Proc. 30th USENIX Security
Symposium, 2021, pp. 1325–1341.
23. S. Bobadilla, M. Jin and M. Monperrus, “Do automated fixes truly mitigate smart contract exploits?,” IEEE Trans. Software
Engineering, 2025, doi: 10.1109/TSE.2025.3618123.
24. I. Beltagy, M. E. Peters and A. Cohan, “Longformer: The long-document transformer,” arXiv: 2004.05150, 2020.
25. Y. Jiang, X. Zhang, C. Gao, C. Zhi, L. Fan et al., “StagedVulBERT: Multi-granular vulnerability detection with a pre-trained code
model,” IEEE Trans. Software Engineering, 2024.
26. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
27. D. Bahdanau, K. Cho and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proc. 3rd Int. Conf. on
Learning Representations (ICLR), 2015.
28. M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov and M. Welling, “Modeling relational data with graph convolutional
networks,” in Proc. 15th Extended Semantic Web Conf. (ESWC), 2018, pp. 593–607.
29. T.-Y. Lin, P. Goyal, R. Girshick, K. He and P. Dollár, “Focal loss for dense object detection,” in Proc. IEEE Int. Conf. on Computer
Vision (ICCV), 2017, pp. 2980–2988.
30. I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proc. 7th Int. Conf. on Learning Representations (ICLR),
2019.

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