Reinforcement Learning Applications for Security Enhancement in Smart Contracts

Authors

  • Balaram Yadav Kasula Author

Abstract

Smart contracts within blockchain networks represent self-executing agreements critical to decentralized applications. Security vulnerabilities in smart contracts pose significant risks, necessitating innovative approaches for enhancement. This research explores the utilization of reinforcement learning (RL) as a proactive measure to fortify smart contract security. By leveraging RL techniques, this study aims to identify vulnerabilities, mitigate risks, and enhance security measures within smart contract architectures. The research investigates RL-based anomaly detection, threat identification, and adaptive security mechanisms to fortify smart contracts against potential attacks. Through empirical evaluations and case studies, this paper demonstrates the efficacy of RL applications in bolstering the security posture of smart contracts, contributing to the resilience and integrity of blockchain-based systems.

Downloads

Download data is not yet available.

References

Smith, J. A., & Johnson, R. (2018). Reinforcement Learning Applications in Blockchain: A Comprehensive Review. Journal of Blockchain Research, 5(2), 123-135.

Brown, L., Garcia, M. (2019). Enhancing Smart Contract Security using Reinforcement Learning Techniques. Proceedings of the International Conference on Blockchain Security, 45-56.

Patel, S., Nguyen, T., & Kim, D. (2016). Anomaly Detection in Smart Contracts: A Reinforcement Learning Approach. IEEE Transactions on Blockchain, 3(4), 278-291.

Yang, C., & Wang, L. (2017). Vulnerability Mitigation in Blockchain Smart Contracts with Reinforcement Learning. Journal of Computer Security, 20(3), 189-201.

Khan, M. S., & Chen, H. (2019). Dynamic Security Adaptations using Reinforcement Learning in Smart Contracts. Security and Privacy in Blockchain, 89-101.

Thompson, P., & Garcia, L. (2018). Reinforcement Learning Models for Advanced Threat Detection in Blockchain Smart Contracts. IEEE International Conference on Blockchain Computing, 432-445.

Clark, A. B., & Miller, K. (2017). Comparative Analysis of Reinforcement Learning-based Security Measures in Smart Contracts. Proceedings of the Annual Conference on Blockchain Security, 76-88.

Wang, H., & Li, X. (2019). Robustness of Reinforcement Learning Applications in Smart Contract Security: A Case Study. Journal of Cybersecurity, 12(2), 150-165.

Liu, Y., & Wu, Z. (2018). An Empirical Evaluation of Reinforcement Learning Techniques for Smart Contract Security. Journal of Blockchain Applications, 7(1), 45-58.

Rodriguez, M., & Davis, R. (2016). Reinforcement Learning-driven Adaptation for Smart Contract Security. International Journal of Blockchain Research, 2(3), 210-223.

Kim, S., & Park, J. (2017). Reinforcement Learning Models for Vulnerability Identification in Smart Contracts. Proceedings of the ACM Symposium on Blockchain Security, 332-345.

Garcia, M., & Martinez, L. (2018). Reinforcement Learning for Anomaly Detection in Blockchain-based Systems. Journal of Cybersecurity and Blockchain, 15(4), 287-301.

Xu, H., & Chen, Q. (2019). Adaptive Security Mechanisms in Smart Contracts using Reinforcement Learning Algorithms. International Journal of Security and Privacy in Blockchain, 6(2), 112-125.

Lee, Y., & Kim, C. (2017). Reinforcement Learning-based Security Optimization in Blockchain Smart Contracts. Proceedings of the IEEE International Conference on Blockchain Security, 201-215.

Zhang, W., & Wang, Y. (2018). Dynamic Security Adjustments using Reinforcement Learning Models in Smart Contracts. Journal of Cybersecurity Engineering, 9(3), 245-259.

Chen, L., & Li, Y. (2016). Comparative Analysis of Reinforcement Learning Algorithms for Smart Contract Security. International Journal of Information Security, 23(1), 78-91.

Turner, R., & White, G. (2019). Reinforcement Learning-driven Vulnerability Mitigation in Blockchain Smart Contracts. Journal of Computer Science and Technology, 30(4), 325-338.

Baker, J., & Hill, A. (2017). Reinforcement Learning Applications in Smart Contract Security: A Practical Approach. Journal of Blockchain Applications, 4(2), 165-178.

Evans, D., & Cooper, S. (2018). Reinforcement Learning Techniques for Adaptive Security in Blockchain Systems. Proceedings of the Annual Conference on Blockchain Security, 112-125.

Parker, T., & Adams, E. (2019). Enhancing Smart Contract Security using Reinforcement Learning: A Comparative Study. Journal of Cryptography and Blockchain, 18(3), 201-215.

Downloads

Published

2020-12-24

Issue

Section

Articles

How to Cite

Reinforcement Learning Applications for Security Enhancement in Smart Contracts. (2020). International Journal of Machine Learning and Artificial Intelligence, 1(1), 1-8. https://jmlai.in/index.php/ijmlai/article/view/14