Deep Learning-Driven Token Semantics for Smart Contract Vulnerability Identification

Smart contracts are self-executing computer-based agreements that are implemented on blockchain systems and that their security is of paramount importance because they are not subject to change. The knowledge of token-level semantics can be helpful in determining the areas that may pose a weakness in these contracts. Nevertheless, current techniques tend to be based on rule based analysis, or syntax level analysis, which find it difficult to reflect the richer semantic structures that result in complex vulnerabilities. In order to overcome these limitations, this paper presents a framework that combines the pretrained transformer functionality of CodeBERT with task-specific fine-tuning and, as such, auto-detects and highlights vulnerabilities in smart contract Integrated Development Environment (IDEs). The method is an examination of token-level semantics, making it possible to identify vulnerabilities and understand them correctly in context. This framework can be applied directly in real-time to IDEs by developers to get vulnerability notifications and recommendations. The experimental outcomes prove that FTC-BERT is much more effective in detecting vulnerabilities and remembering experiments than traditional, and it is a semantic-sensitive, efficient, and automated method to detect vulnerabilities in smart contracts.

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