Detecting Re-Entrancy Attacks in Ethereum via Dynamic Tracing

Abstract

One of the most promising technology of blockchain still suffers from multiple attacks. Attack detection is handled by various technologies to enhance the security. Hackers moto is to steal the digital currency here the discussion is of the digital currency Ethereum which also has suffered from the famous attack Re-entrancy attack. Though the usage of smart contact is highly believed by users, making the function to be called again and again leads to this kind of attack. Re-entrancy attacks pose a persistent threat to Ethereum smart contracts, enabling malicious users to repeatedly call vulnerable functions before the state variables are correctly updated. While traditional approaches rely heavily on symbolic execution and SMT solvers for vulnerability detection, these methods suffer from scalability and precision limitations. A novel hybrid framework that integrates dynamic execution tracing with AI-driven classification models, such as Quantum Neural Networks (QNN), for the robust detection of re-entrancy attacks. The system leverages taint analysis and runtime monitoring to generate behavioral features, which are then used to train and evaluate machine learning models. Experiments conducted on benchmark datasets demonstrate superior accuracy and reduced false positives when compared to conventional static analysis techniques, showcasing the effectiveness of combining dynamic and AI-based strategies for smart contract security.