Enhancing Smart Contract Security Through Multi-Agent Deep Reinforcement Learning Fuzzing: A Survey of Approaches and Techniques

Abstract

Multi-Agent Systems (MAS) and Deep Reinforcement Learning (DRL) have emerged as powerful tools for enhancing security measures, particularly in the context of smart contract security in blockchain technology. This literature review explores the integration of Multi-Agent DRL fuzzing techniques to bolster the security of smart contracts. The study delves into the formalization of emergence in MAS, the comprehensive survey of multi-agent reinforcement learning, and progress on the state explosion problem in model checking. By addressing challenges such as state space explosion, real-time detection, and adaptability across blockchain platforms, researchers aim to advance the field of smart contract security. The review emphasizes the significance of Multi-Agent DRL fuzzing in improving security testing processes and calls for future research and collaboration to enhance the resilience and integrity of decentralized applications. Through advancements in algorithmic efficiency, the incorporation of Explainable AI, cross-domain applications of MAS, and cooperation with blockchain development teams, the future of smart contract security holds promise for robust and secure blockchain ecosystems.