Smart Contract Security in Decentralized Finance: Enhancing Vulnerability Detection with Reinforcement Learning

The growing interest in decentralized finance (DeFi), driven by advancements in blockchain technologies such as Ethereum, highlights the crucial role of smart contracts. However, the inherent openness of blockchains creates an extensive attack surface, exposing participants’ funds to undetected security flaws. In this work we investigate the use of deep reinforcement learning techniques, specifically Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), for detecting and classifying vulnerabilities in smart contracts. This approach utilizes control flow graphs (CFGs) generated through EtherSolve to capture the semantic features of contract bytecode, enabling the reinforcement learning models to recognize patterns and make more accurate predictions. Experimental results from extensive public datasets of smart contracts reveal that the PPO model performs better than DQN and demonstrates effectiveness in identifying unchecked-call vulnerability. The PPO model exhibits more stable and consistent learning patterns and achieves higher overall rewards. This research introduces a machine learning method for enhancing smart contract security, reducing financial risks for users, and contributing to future developments in reinforcement learning applications.