Reinforcement Learning Applications for Security Enhancement in Smart Contracts
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.
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References
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