이더리움 스마트컨트랙트 가스 제한 취약점 방지를 위한 Unified Random Forest 적용 실시간 탐지 기법
A Scheme Real-Time Detection Technique using Unified Random Forest to prevent Ethereum Smart Contract Gas Limit Vulnerabilities
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초록
이더리움은 스마트컨트랙트를 활용하는 여러 암호화폐 중 하나로, 오늘날 대표적으로 잘 알려진 암호화폐이다. 스마트컨트랙트로 인해, 보안과 거래 속도, 투명성 및 신뢰성을 보장하며 블록체인 기술에서 안전한 암호화폐 거래가가능하게 한다. 하지만, 스마트컨트랙트에서도 치명적인 취약점은 존재하며, 이를 억제하고 보완하기 위한 기술들이 개발되고 있다. 그중 이더리움 네트워크에서 발생하는 중요한 문제점 중 하나인 가스 제한(Gas Limit) 취약점이 존재한다. 가스 제한 취약점은 이더리움에서 스마트컨트랙트가 실행될 때 요구되는 수수료로 사용되지만, 이 가스 비용을 조작하여 계약 중단을 유도하거나, 최적화 방해, DoS 공격을 유발할 수 있다. 이를 통해 피해자의 가스만 소모하고, 네트워크 혼잡을 일으켜 피해를 준다. 본 논문에서는 Unified Random Forest 모델을 활용하여, 이더리움 네트워크에서 발생하는 트랜잭션 데이터를 실시간으로 탐지하여 가스 제한 취약점을 방지하는 기법을 제안하고자 한다. 해당 기법을통해, 무분별한 가스 손실을 최소화하고, 비이상적인 트랜잭션을 탐지하여 사전에 가스 제한 취약점을 막을 수 있다.
Abstract
Ethereum is one of the many cryptocurrencies that use smart contracts, and it is a well-known cryptocurrency today. Due to smart contracts, security, transaction speed, transparency and reliability are guaranteed, and secure cryptocurrency transactions are possible in blockchain technology. However, there are fatal vulnerabilities in smart contracts, and technologies are being developed to suppress and supplement them. Among them, one of the important problems occurring in the Ethereum network, a vulnerability to gas limit, exists. A gas limit vulnerability is used as a fee required when a smart contract is executed in Ethereum, but it can manipulate this gas cost to induce contract suspension, interfere with optimization, and cause DoS attacks. This consumes only the victim's gas, causes network congestion, and damages. In this paper, we propose a technique to prevent gas limitation vulnerabilities by detecting transaction data occurring in the Ethereum network in real time. Through this technique, it is possible to minimize indiscriminate gas loss and prevent gas limitation vulnerabilities in advance by detecting abnormal transactions.
참고문헌 (15)
- "OWASP Smart Contract Top 10"[URL]
- J. -P. Schmitten, "Decentralized Blockchain Governance and Transaction Costs in Digital Transformation : The Case of the DAO Revisited", 2023 Portland International Conference on Management of Engineering and Technology (PICMET), pp.1~14 (2023)
- "Ethereum Merge"[URL]
- W. Chan, "Ethereum transaction graph analysis", 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), pp.498~500 (2017)
- L. S. H. Colin, "An Integrated Smart Contract Vulnerability Detection Tool Using Multi-Layer Perceptron on Real-Time Solidity Smart Contracts", IEEE Access, 12, pp.23549~23567 (2024)
- D. He, "Smart Contract Vulnerability Analysis and Security Audit", IEEE Network, 34(5), pp.276~282 (2020)
- R. Xie, "Ethereum-Blockchain-Based Technology of Decentralized Smart Contract Certificate System", IEEE Internet of Things Magazine, 3(2), pp.44~50 (2020)
- S. Wang, "Blockchain-Enabled Smart Contracts : Architecture, Applications, and Future Trends", IEEE Transactions on Systems, Man, and Cybernetics : Systems, 49(11), pp.2266~2277 (2019)
- K. Kaur, "Gas Fee Reduction by Detecting Loop Fusible Patterns in Ethereum Smart Contract", 2022 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp.458~463 (2022)
- B. D. Perera, "Optimizing Gas Fees for Cost-Effective E-voting Smart Contracts on the Ethereum Blockchain", 20235th International Conference on Advancements in Computing (ICAC), pp.7~11 (2023)
- F. Liu, "Effective GasPrice Prediction for Carrying Out Economical Ethereum Transaction", 2019 6th International Conference on Dependable Systems and Their Applications (DSA), pp.329~334 (2020)
- A. M. Fajge, "Wait or Reset Gas Price? : A Machine Learning-based Prediction Model for Ethereum Transactions' Waiting Time", 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp.1153~1160 (2021)
- D. Jim Solomon Raja, "Unified Random Forest and Hybrid Bat Optimization Based Man-in-the-Middle Attack Detection in Advanced Metering Infrastructure", IEEE Transactions on Instrumentation and Measurement, 73(2523812), pp.1~12 (2024)
- D. Banerjee, "Smart Disease Detection : Unifying CNNs and Random Forests for Accurate Pear Leaf Classification", 20243rd International Conference for Innovation in Technology (INOCON) (2024)
- S. A. Roseline, "Towards Efficient Malware Detection and Classification using Multilayered Random Forest Ensemble Technique", 2019 International Carnahan Conference on Security Technology (ICCST), pp.1~6 (2019)