AI-Driven Smart Contract Security: A Deep Learning Approach to Vulnerability Detection

Abstract

Smart contracts, which allow for decentralized, automated transactions on blockchains, have been the source of repeated financial loss from hacking and coding flaws. This article introduces an AI-based deep learning approach to automated detection of vulnerabilities in smart contracts on Ethereum. The architecture integrates code- token embeddings (CodeBERT-style), control- and data-flow graph representations, and a hierarchical graph neural network (HGNN) with attention-based multimodal fusion to allow for comprehensive understanding of human-written programs. We train on labelled datasets from real-world contracts, utilising data augmentation and addressing class imbalance (focal loss + over sampling). For the experimental study, we compare the performance of our framework with existing solely-static and sequence-based transformers approaches apart from other GNN models on public datasets; ScrawlD, SmartBugs and manually curated Github-derived samples. Results The fused HGNN model performs with an average F1-score of 0.91, precision of 0.89, recall of 0.93 and AUC of 0.95 better than transformer- only (F1 = 0.86) and static-tool baselines (F1 = 0.71). The method shows strong generality to a wide range of vulnerability forms (reentrancy, integer overflow, unchecked calls, access control bugs) and enhances the precision for function-level localization. We further develop an interpretation module to map attention weights back to AST/CFG regions for human auditors. The paper also addresses limitations on dataset bias, obfuscation-resilience and adversarial examples and provides ideas for further investigation such as few-shot adaptation with one-class VAEs, integration with continuous deployment pipelines. The contributions: a multimodal deep-learning model for vulnerability detection and localization, an empirical study on state-of-the-art performance in multiple benchmark projects with large amounts of code; and advice how to deploy the AI-assisted contract auditing in development workflows.