ContractWard: Automated Vulnerability Detection Models for Ethereum Smart Contracts
Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an e...
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| Published in | IEEE transactions on network science and engineering Vol. 8; no. 2; pp. 1133 - 1144 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets. |
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| AbstractList | Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets. |
| Author | Xu, Guangquan Wang, Wei Li, Yidong Su, Chunhua Song, Jingjing Wang, Hao |
| Author_xml | – sequence: 1 givenname: Wei orcidid: 0000-0002-5974-1589 surname: Wang fullname: Wang, Wei email: wangwei1@bjtu.edu.cn organization: Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China – sequence: 2 givenname: Jingjing orcidid: 0000-0002-1098-0511 surname: Song fullname: Song, Jingjing email: 17120479@bjtu.edu.cn organization: Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China – sequence: 3 givenname: Guangquan orcidid: 0000-0001-8701-3944 surname: Xu fullname: Xu, Guangquan email: losin@tju.edu.cn organization: Tianjin Key Laboratory of Advanced Networking, College of Intelligence and Computing, Tianjin University, Tianjin, China – sequence: 4 givenname: Yidong orcidid: 0000-0003-2965-6196 surname: Li fullname: Li, Yidong email: ydli@bjtu.edu.cn organization: Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, China – sequence: 5 givenname: Hao orcidid: 0000-0001-9301-5989 surname: Wang fullname: Wang, Hao email: hawa@ntnu.no organization: Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, Norway – sequence: 6 givenname: Chunhua orcidid: 0000-0002-6461-9684 surname: Su fullname: Su, Chunhua email: suchunhua@gmail.com organization: Division of Computer Science, University of Aizu, Aizu-Wakamatsu, Japan |
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| Snippet | Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security... |
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| SubjectTerms | Algorithms Blockchain Contracts Cryptography Feature extraction Flaw detection Machine learning Machine learning algorithms Security Smart contracts Training vulnerability detection |
| Title | ContractWard: Automated Vulnerability Detection Models for Ethereum Smart Contracts |
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