Smart Contract Security - A Comprehensive Evaluation of Deep Learning based Mechanisms for Smart Contract Vulnerability Detection

Abstract

Widespread adoption of Blockchain Technology has emphasized smart contracts as vital units of digital transactions. In the brief history of smart contracts, significant losses have occurred owing to unexplained vulnerabilities in blockchain-loaded contracts. Smart contract vulnerabilities endanger blockchain technology's viability and confidence. Blockchain-based applications rely on smart contracts to automate and trustlessly execute agreements. Their immutable and self-executing nature leaves them vulnerable to security vulnerabilities, which have caused major financial losses in decentralized platforms. Traditional static and symbolic analysis tools often miss sophisticated or obfuscated vulnerabilities, resulting in insufficient coverage or excessive false positives. These restrictions led the investigation of Deep Learning (DL) algorithms for detecting vulnerabilities in smart contract crucial since they can learn complicated patterns from code representations without manual feature engineering. Deep learning based methods for detecting vulnerabilities in smart contracts are thoroughly evaluated. We rigorously assess state-of-the-art DL models including BiLSTM, BiGRU, CNNLSTM, GCN, and CodeBERT using publicly available dataset. We provide a taxonomy of DL-based detection techniques and standardize performance criteria including accuracy, F1-score, and detection latency. Experimental results show model architecture trade-offs in detection, computational efficiency, and generalization.