Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2
Conference on Automated Software Engineering (ASE '22). Association for Computing Machinery, New York, NY, USA, Article 83, 1–13. https://doi. org/10.1145/3551349.3560428. 4. Xiaobing Sun, Liangqiong Tu, Jiale Zhang, Jie Cai, Bin Li, and Yu Wang. 2023. ASSBert: Active and semi-supervised bert for smart contract vulnerability detection. J. Inf. Secur. Appl. 73, C (Mar 2023). https://doi.org/10.1016/j.jisa.2023.103423. 5. Jianzhong Su, Hong-Ning Dai, Lingjun Zhao, Zibin Zheng, and Xiapu Luo. 2023. Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE '22). Association for Computing Machinery, New York, NY, USA, Article 36, 1–12. https://doi.org/10.1145/3551349.3560429 . 6. Z. Liu, P. Qian, X. Wang, Y. Zhuang, L. Qiu and X. Wang, "Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection," in IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 2, pp. 1296-1310, 1 Feb. 2023, doi: 10.1109/TKDE.2021.3095196. 7. S. Shakya, A. Mukherjee, R. Halder, A. Maiti and A. Chaturvedi, "SmartMixModel: Machine Learning- based Vulnerability Detection of Solidity Smart Contracts," 2022 IEEE International Conference on Blockchain (Blockchain), Espoo, Finland, 2022, pp. 37-44, doi: 10.1109/Blockchain55522.2022.00016. 8. Taherdoost, H. Smart Contracts in Blockchain Technology: A Critical Review. Information 2023, 14 , 117. https://doi.org/10.3390/info14020117 . 9. S. K. Sood, A. K. Sarje and K. Singh, "An improvement of Wang et al.'s authentication scheme using smart cards," 2010 National Conference On Communications (NCC), Chennai, India, 2010, pp. 1-5, doi:10.1109/NCC.2010.5430153. 10. K. Wüst and A. Gervais, “Do You Need a Blockchain?” in 2018 Crypto Valley Conference on Blockchain Technology (CVCBT). IEEE, 2018, pp. 45–54. 11. C. D. Clack, V. A. Bakshi, and L. Braine, “Smart Contract Tem- plates: Essential Requirements and Design Options,” arXiv preprint arXiv:1612.04496, 2016. 12. S. Wang, Y. Yuan, X. Wang, J. Li, R. Qin, and F.-Y. Wang, “An Overview of Smart Contract: Architecture, Applications, and Future Trends,” in 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018, pp. 108– 113. 13. N. Reiff, “What Is ERC-20 and What Does It Mean for Ethereum?” 2020. [Online]. Available: https:// www.investopedia.com/news/what-erc20-and-what- does-it-mean-ethereum/ 14. Z. Zheng, S. Xie, H.-N. Dai, W. Chen, X. Chen, J. Weng, and M. Imran, “An overview on smart contracts: Challenges, advances and platforms,” Future Generation Computer Systems, vol. 105, pp. 475–491, 2020. 15. Deep learning-based malicious smart contract detection scheme for internet of things environment. (2021, November 20). Deep Learning-based Malicious Smart Contract Detection Scheme for Internet of Things Environment - ScienceDirect. https://doi.org/10.1016/j.compeleceng.2021.107583 16. D. He, R. Wu, X. Li, S. Chan and M. Guizani, "Detection of Vulnerabilities of Blockchain Smart Contracts," in IEEE Internet of Things Journal, vol. 10, no. 14, pp. 12178-12185, 15 July15, 2023, doi: 10.1109/JIOT.2023.3241544. 17. Y. Huang, Y. Bian, R. Li, J. L. Zhao and P. Shi, "Smart Contract Security: A Software Lifecycle Perspective," in IEEE Access, vol. 7, pp. 150184-150202, 2019, doi: 10.1109/ACCESS.2019.2946988. 18. How Security Analysts Can Use AI in Cybersecurity. (2023, May 24). freeCodeCamp.org. https://www. freecodecamp.org/news/how-to-use-artificial-intelli gence-in-cybersecurity/ 19. Artificial Intelligence (AI) vs. Machine Learning. (n.d.). CU-CAI. https://ai.engineering.columbia.edu/ ai-vs-machine-learning/ 20. Sarker, I. H., Furhad, M. H., &Nowrozy, R. (2021, March 26). AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions - SN Computer Science. SpringerLink. https:// doi.org/10.1007/s42979-021-00557-0. 21. Deep reinforcement learning for blockchain in industrial IoT: A survey. (2021, March 18). Deep Reinforcement Learning for Blockchain in Industrial IoT: A Survey - ScienceDirect. https://doi.org/10.10 16/j.comnet.2021.108004. 22. Fei, J., Chen, X., & Zhao, X. (2023, January 29). MSmart: Smart Contract Vulnerability Analysis and Improved Strategies Based on Smartcheck. MDPI. https://doi.org/10.3390/app13031733. 23. H., Xu, Y., Hu, G., You, L., & Cao, C. (2021, August 15). A Novel Machine Learning-Based Analysis Model for Smart Contract Vulnerability. A Novel Machine Learning-Based Analysis Model for Smart Contract Vulnerability. https://doi.org/10.1155/2021 /5798033. 24. Liu, Z., Qian, P., Wang, X., Zhu, L., He, Q., & Ji, S. (2021, June 17). Smart Contract Vulnerability Detection: From Pure Neural Network to Interpretable Graph Feature and Expert Pattern Fusion. arXiv.org. https://arxiv.org/abs/2106.09282 v1. 25. OWASP Smart Contract Top 10 | OWASP Foundation. (n.d.). OWASP Smart Contract Top 10 | OWASP Foundation. https://owasp.org/www-project - smart-contract-top-10/ Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 66 ( ) Year 2023 D Strengthening Smart Contracts: An AI-Driven Security Exploration © 2023 Global Journals
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