Detection of Re-Entrancy, Timestamp Dependence and Infinite Loop Attack in Smart Contracts Using Graph Convolution Network


  • D. Saveetha SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India
  • G. Maragatham Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur 603203, Tamil Nadu, India


Smart contract, vulnerability, Graph Convolution Network, re-entrancy, time stamp dependence attack, infinite loop attack


Smart Contract Attack Detection Using Graph Convolution Network (GCN) is a research area that focuses on identifying and preventing malicious activities within smart contracts deployed on blockchain platforms. Smart contracts are self-executing digital agreements that run on decentralized networks, such as Ethereum. While smart contracts provide transparency and automation, they can also be vulnerable to various attacks, leading to financial losses or system disruptions. To address this challenge, the concept of Graph Convolution Network is leveraged. GCN is a deep learning technique that operates on graph-structured data, where nodes represent entities, and edges represent relationships between them. In the context of smart contracts, a graph can be constructed to capture the dependencies between different functions, variables, and transactions within the contract. The goal of utilizing GCN in smart contract attack detection is to learn patterns and detect anomalies in the graph structure. By training the model on a large dataset of known secure and malicious smart contracts, it can learn to identify suspicious patterns that might indicate an ongoing attack. The GCN model can consider features such as function calls, control flow, and data dependencies to detect potential vulnerabilities or abnormal behavior. In this paper we are going to address the detection of reentrancy attack, timestamp dependence attack and infinite loop attack using Graph Convolution Network. Smartbugs wild dataset is used for performing the attack detection. By using GCN we are able to detect these attacks accurately and our model is compared with the existing models and it shows that our model is better than the existing models in terms of performance metrics.


Download data is not yet available.


Priyanka Bose, Dipanjan Das, Yanju Chen, Yu Feng, Christopher Kruegel, and Giovanni Vigna University of California, Santa Barbara,IEEE,2022.

Noama Fatima Samreen, Manar H. Alalfi ,A Survey of Security Vulnerabilities in Ethereum Smart Contracts,


Lejun Zhang,Weijie Chen,Weizheng Wang ,Zilong Jin, Chunhui Zhao, Zhennao Cai and Huiling Chen,”CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model“, Sensors , Vol 22, 3577,2022.

Jianbo Gao, Han Liu, Yue Li, Chao Liu,

Zhiqiang Yang, Qingshan Li, Zhi Guan, Zhong Chen,” Towards automated testing of blockchain-based decentralized applications,” ICPC '19: Proceedings of the 27th International Conference on Program Comprehension,May 2019.

Qian, Peng, Zhenguang Liu, Qinming He, Butian Huang, Duanzheng Tian, and Xun Wang. "Smart Contract Vulnerability Detection Technique: A Survey." arXiv preprint arXiv:2209.05872 (2022).

Zhang, L.; Wang, J.; Wang, W.; Jin, Z.; Zhao, C.; Cai, Z.; Chen, H. A Novel Smart Contract Vulnerability Detection Method Based on Information Graph and Ensemble Learning,Sensors 2022.

H. Wu et al., "Peculiar: Smart Contract Vulnerability Detection Based on Crucial Data Flow Graph and Pre-training Techniques," 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE), 2021, pp. 378-389, doi: 10.1109/ISSRE52982.2021.00047.

Jiaming Ye, Mingliang Ma, Yun Lin, Lei Ma, Yinxing Xue, Jianjun Zhao,Vulpedia: Detecting vulnerable ethereum smart contracts via abstracted vulnerability signatures,Journal of Systems and Software, Volume 192, 2022.

Yuan Zhuang, Zhenguang Liu, Peng Qian, Qi Liu, Xiang Wang, and Qinming He.,” Smart contract vulnerability detection using graph neural networks” Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI'20). Article 454, 3283–3290,2021.

N. Atzei, M. Bartoletti, and T. Cimoli. A survey of attacks on ethereum smart contracts (sok). In Principles of Security and Trust”, pages 164–186. Springer, 2017.

Jennifer.j.Xu “,Are blockchains immune to all malicious attacks?”,2016 Xu Financial Inclusion.

S.Nakamoto,”Bitcoin: A peer-to-peer electronic cash system”,2008.

Yuichiro Chinen, Naoto Yanai, Jason Paul Cruz, Shingo Okamura,” Hunting for Re-Entrancy Attacks in Ethereum Smart Contracts via Static Analysis”, IEEE Blockchain 2020.

Daojun Han, Qiuyue Li,Lei Zhang and Tao Xu ,”A Smart Contract Vulnerability Detection Model Based on Syntactic and Semantic Fusion Learning”, Wireless Communications and Mobile Computing, Vol 2023, Article ID 9212269,2023.

Jing Huang, Kuo Zhou, Ao Xiong, Dongmeng Li,” Smart Contract Vulnerability Detection Model Based on Multi-Task Learning“, Sensors, Vol 22, 1829, 2022

Kumar, S. A. S., Naveen, R., Dhabliya, D., Shankar, B. M., & Rajesh, B. N. (2020). Electronic currency note sterilizer machine. Paper presented at the Materials Today: Proceedings,37(Part 2) 1442-1444. doi:10.1016/j.matpr.2020.07.064 Retrieved from

Wanjiku, M., Ben-David, Y., Costa, R., Joo-young, L., & Yamamoto, T. Automated Speech Recognition using Deep Learning Techniques. Kuwait Journal of Machine Learning, 1(3). Retrieved from

Elena Petrova, Predictive Analytics for Customer Churn in Telecommunications , Machine Learning Applications Conference Proceedings, Vol 1 2021.




How to Cite

Saveetha, D. ., & Maragatham, G. . (2023). Detection of Re-Entrancy, Timestamp Dependence and Infinite Loop Attack in Smart Contracts Using Graph Convolution Network. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 285–292. Retrieved from



Research Article