How to cite this paper
AlShorman, A., Shannaq, F & Shehab, M. (2024). Machine learning approaches for enhancing smart contracts security: A systematic literature review.International Journal of Data and Network Science, 8(3), 1349-1368.
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Aziz, R. M., Baluch, M. F., Patel, S., & Kumar, P. (2022). A machine learning based approach to detect the Ethereum fraud transactions with limited attributes. Karbala International Journal of Modern Science, 8(2), 139-151.
Cai, J., Li, B., Zhang, J., Sun, X., & Chen, B. (2023). Combine sliced joint graph with graph neural networks for smart con-tract vulnerability detection. Journal of Systems and Software, 195, 111550.
Chen, H., Pendleton, M., Njilla, L., & Xu, S. (2020). A survey on ethereum systems security: Vulnerabilities, attacks, and de-fenses. ACM Computing Surveys (CSUR), 53(3), 1-43.
Chen, J., Xia, X., Lo, D., Grundy, J., & Yang, X. (2021). Maintenance-related concerns for post-deployed Ethereum smart contract development: issues, techniques, and future challenges. Empirical Software Engineering, 26(6), 117.
Chithanuru, V., & Ramaiah, M. (2023). An anomaly detection on blockchain infrastructure using artificial intelligence tech-niques: Challenges and future directions–A review. Concurrency and Computation: Practice and Experience, 35(22), e7724.
de la Rosa, J. L., Gibovic, D., Torres, V., Maicher, L., Miralles, F., El-Fakdi, A., & Bikfalvi, A. (2016, December). On intellec-tual property in online open innovation for SME by means of blockchain and smart contracts. In 3rd Annual World Open Innovation Conf. WOIC.
Durieux, T., Ferreira, J. F., Abreu, R., & Cruz, P. (2020, June). Empirical review of automated analysis tools on 47,587 ethereum smart contracts. In Proceedings of the ACM/IEEE 42nd International conference on software engineering (pp. 530-541).
Eduardo A. Sousa, J., Oliveira, V. C., Almeida Valadares, J., Borges Vieira, A., Bernardino, H. S., Moraes Villela, S., & Dias Goncalves, G. (2021). Fighting under-price DoS attack in ethereum with machine learning techniques. ACM SIGMETRICS Performance Evaluation Review, 48(4), 24-27.
Eshghie, M., Artho, C., & Gurov, D. (2021, June). Dynamic vulnerability detection on smart contracts using machine learn-ing. In Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering (pp. 305-312).
Essaid, M., Kim, D., Maeng, S. H., Park, S., & Ju, H. T. (2019, September). A collaborative DDoS mitigation solution based on ethereum smart contract and RNN-LSTM. In 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS) (pp. 1-6). IEEE.
Fang, L., Zhao, B., Li, Y., Liu, Z., Ge, C., & Meng, W. (2020). Countermeasure based on smart contracts and AI against DoS/DDoS attack in 5G circumstances. IEEE Network, 34(6), 54-61.
Garg, K., Saraswat, P., Bisht, S., Aggarwal, S. K., Kothuri, S. K., & Gupta, S. (2019, April). A comparitive analysis on e-voting system using blockchain. In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU) (pp. 1-4). IEEE.
Gaur, R., Prakash, S., Kumar, S., Abhishek, K., Msahli, M., & Wahid, A. (2022). A machine-learning–blockchain-based au-thentication using smart contracts for an ioht system. Sensors, 22(23), 9074.
Gogineni, A. K., Swayamjyoti, S., Sahoo, D., Sahu, K. K., & Kishore, R. (2020). Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing. IOP SciNotes, 1(3), 035002.
Goswami, S., Singh, R., Saikia, N., Bora, K. K., & Sharma, U. (2021, August). TokenCheck: towards deep learning based se-curity vulnerability detection in ERC-20 tokens. In 2021 IEEE Region 10 Symposium (TENSYMP) (pp. 1-8). IEEE.
Grishchenko, I., Maffei, M., & Schneidewind, C. (2018). Foundations and tools for the static analysis of ethereum smart con-tracts. In Computer Aided Verification: 30th International Conference, CAV 2018, Held as Part of the Federated Logic Conference, FloC 2018, Oxford, UK, July 14-17, 2018, Proceedings, Part I 30 (pp. 51-78). Springer International Publish-ing.
Gupta, R., Patel, M. M., Shukla, A., & Tanwar, S. (2022). Deep learning-based malicious smart contract detection scheme for internet of things environment. Computers & Electrical Engineering, 97, 107583.
Harz, D., & Knottenbelt, W. (2018). Towards safer smart contracts: A survey of languages and verification methods. arXiv preprint arXiv:1809.09805.
Hasanova, H., Baek, U. J., Shin, M. G., Cho, K., & Kim, M. S. (2019). A survey on blockchain cybersecurity vulnerabilities and possible countermeasures. International Journal of Network Management, 29(2), e2060.
Huang, Y., Bian, Y., Li, R., Zhao, J. L., & Shi, P. (2019). Smart contract security: A software lifecycle perspective. IEEE Ac-cess, 7, 150184-150202.
Hwang, S. J., Choi, S. H., Shin, J., & Choi, Y. H. (2022). CodeNet: Code-targeted convolutional neural network architecture for smart contract vulnerability detection. IEEE Access, 10, 32595-32607.
Ibba, G., Pierro, G. A., & Di Francesco, M. (2021, May). Evaluating machine-learning techniques for detecting smart ponzi schemes. In 2021 IEEE/ACM 4th International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB) (pp. 34-40). IEEE.
Imperius, N. P., & Alahmar, A. D. (2022). Systematic Mapping of Testing Smart Contracts for Blockchain Applica-tions. IEEE Access, 10, 112845-112857.
Ivanov, N., Li, C., Yan, Q., Sun, Z., Cao, Z., & Luo, X. (2023). Security threat mitigation for smart contracts: A comprehen-sive survey. ACM Computing Surveys, 55(14s), 1-37.
Jiang, F., Chao, K., Xiao, J., Liu, Q., Gu, K., Wu, J., & Cao, Y. (2023). Enhancing smart-contract security through machine learning: A survey of approaches and techniques. Electronics, 12(9), 2046.
Jie, W., Chen, Q., Wang, J., Koe, A. S. V., Li, J., Huang, P., ... & Wang, Y. (2023). A novel extended multimodal AI frame-work towards vulnerability detection in smart contracts. Information Sciences, 636, 118907.
Keele, S. (2007). Guidelines for performing systematic literature reviews in software engineering.
Kim, Y., Pak, D., & Lee, J. (2019). ScanAT: identification of bytecode-only smart contracts with multiple attribute tags. IEEE Access, 7, 98669-98683.
Kirillov, D., Iakushkin, O., Korkhov, V., & Petrunin, V. (2019). Evaluation of tools for analyzing smart contracts in distrib-uted ledger technologies. In Computational Science and Its Applications–ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part II 19 (pp. 522-536). Springer International Publishing.
Kirli, D., Couraud, B., Robu, V., Salgado-Bravo, M., Norbu, S., Andoni, M., ... & Kiprakis, A. (2022). Smart contracts in energy systems: A systematic review of fundamental approaches and implementations. Renewable and Sustainable Energy Reviews, 158, 112013.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26.
Kushwaha, S. S., Joshi, S., Singh, D., Kaur, M., & Lee, H. N. (2022). Systematic review of security vulnerabilities in ethere-um blockchain smart contract. IEEE Access, 10, 6605-6621.
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