AAPSCH: Adaptive AI-Powered Smart Contract Honeypots with Reinforcement Learning for Blockchain Security

Abstract

Blockchain ecosystems, particularly decentralized finance (DeFi) platforms, have become high-value targets for adversaries exploiting vulnerabilities in smart contracts. Traditional honeypots designed to attract such attackers are often static and easily detectable once adversaries recognize repeating patterns. In this paper, we propose Adaptive AI-Powered Smart Contract Honeypots (AASCH), a novel deception-based security mechanism that dynamically evolves its behavior using reinforcement learning techniques. The honeypot continuously adapts to attacker strategies by modifying contract responses, transaction logic, and resource interactions in real time, thereby creating an unpredictable deception environment. We implement AASCH on the Ethereum test network and simulate various exploit scenarios, including reentrancy attacks, flash-loan exploits, and oracle manipulations. Experimental results demonstrate that AASCH significantly improves attacker capture rates (up to 37% higher than static honeypots) while reducing evasion attempts and false positives. Our findings indicate that adaptive deception is a promising defense strategy for strengthening blockchain ecosystems against evolving cyber threats.