Integration of NLP and machine learning methods for smart contract security: a comparison with traditional approaches

In modern blockchain systems, smart contracts are one of the most critical components for ensuring the automated execution of agreements without the need for intermediaries. However, smart contracts written in languages like Solidity may contain vulnerabilities that can be exploited by malicious actors to steal funds or manipulate assets. Given the increasing number of attacks on smart contracts, the development of effective methods for detecting such vulnerabilities is crucial. Traditional approaches to detecting vulnerabilities in smart contracts include symbolic execution, fuzzing, formal verification, and pattern matching. These methods have their advantages but face several challenges, such as high resource consumption, limitations in detecting new types of vulnerabilities, and difficulties in scaling to large contracts. As a result, there is a need to introduce new approaches, such as natural language processing (NLP) and machine learning, which can address these challenges more effectively. In this study, an NLP-based method was explored, using Word2Vec to convert smart contract code into vector representations, allowing for better analysis of the semantic relationships between elements of the code. These vector representations are then fed into a bidirectional recurrent neural network with GRU blocks and an attention mechanism. This approach allows the model to focus on the most important parts of the code and improve the accuracy of vulnerability detection. The comparative analysis showed that NLP-based methods significantly outperform traditional approaches in all key metrics. In particular, the GRU model with an attention mechanism demonstrated high results in accuracy, recall, and F-measure, making it effective for detecting complex vulnerabilities such as reentrancy. Furthermore, the NLP-based approach is capable of adapting to new types of attacks thanks to training on large datasets. Thus, the integration of NLP and machine learning represents a promising direction for enhancing the security of smart contracts. Future research can focus on improving these approaches, particularly through the implementation of advanced models such as transformers.