A Bytecode-based Approach for Smart Contract Classification

TL;DR

This paper introduces a bytecode-based smart contract classification model that overcomes limitations of NLP methods, enabling effective, source-code independent classification with improved performance and resistance to adversarial attacks.

Contribution

The paper presents a novel bytecode-based classification approach using feature selection and ensemble learning, addressing source code availability and security vulnerabilities of NLP methods.

Findings

The model classifies smart contracts without source code.

It outperforms baseline models in accuracy.

It demonstrates robustness against adversarial attacks.

Abstract

With the development of blockchain technologies, the number of smart contracts deployed on blockchain platforms is growing exponentially, which makes it difficult for users to find desired services by manual screening. The automatic classification of smart contracts can provide blockchain users with keyword-based contract searching and helps to manage smart contracts effectively. Current research on smart contract classification focuses on Natural Language Processing (NLP) solutions which are based on contract source code. However, more than 94% of smart contracts are not open-source, so the application scenarios of NLP methods are very limited. Meanwhile, NLP models are vulnerable to adversarial attacks. This paper proposes a classification model based on features from contract bytecode instead of source code to solve these problems. We also use feature selection and ensemble learning to optimize the model. Our experimental studies on over 3,300 real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models. Our model also has good resistance to adversarial attacks compared with NLP-based models. In addition, our analysis reveals that account features used in many smart contract classification models have little effect on classification and can be excluded.