Usage of Static Taint Analysis and Auto Rule Generation for Smart Contract Vulnerability Detection

Abstract

With the advent of Bitcoin, virtual currency and blockchain technology are attracting attention, and the market is growing with the addition of the smart contract function of Blockchain 2.0. With the growth of the blockchain market, financial damage using the vulnerabilities of virtual currency and smart contracts is increasing. Recently, many studies have been conducted to detect smart contract vulnerabilities, and various methods using static and dynamic analysis exist. This paper proposes a method to detect smart contract vulnerabilities using static analysis and association mining techniques. From the experiment, we show that the performance of the proposed method is high compared to well-known open-source tools. We observe the effectiveness of static taint analysis for smart contract vulnerability detection. The proposed method shows high performance against reentrancy vulnerabilities. We expected that rapid response will be possible when a new type of vulnerability occurs in the future.