<i>Block</i><i>-gram:</i> Mining knowledgeable features for efficiently smart contract vulnerability detection

doi:10.1016/j.dcan.2023.07.009

	Block-gram: Mining knowledgeable features for efficiently smart contract vulnerability detection
	Xie, Xueshuo 2,3,4; Wang, Haolong 2; Jian, Zhaolong 2; Fang, Yaozheng 2; Wang, Zichun 2; Li, Tao 1,2,3,4
	2025-02-01
发表期刊	DIGITAL COMMUNICATIONS AND NETWORKS
ISSN	2468-5925
卷号	11 期号:1 页码:1-12
摘要	Smart contracts are widely used on the blockchain to implement complex transactions, such as decentralized applications on Ethereum. Effective vulnerability detection of large-scale smart contracts is critical, as attacks on smart contracts often cause huge economic losses. Since it is difficult to repair and update smart contracts, it is necessary to find the vulnerabilities before they are deployed. However, code analysis, which requires traversal paths, and learning methods, which require many features to be trained, are too time-consuming to detect large-scale on-chain contracts. Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution. But the existing features lack the interpretability of the detection results and training model, even worse, the large-scale feature space also affects the efficiency of detection. This paper focuses on improving the detection efficiency by reducing the dimension of the features, combined with expert knowledge. In this paper, a feature extraction model Block-gram is proposed to form low- dimensional knowledge-based features from bytecode. First, the metadata is separated and the runtime code is converted into a sequence of opcodes, which are divided into segments based on some instructions (jumps, etc.). Then, scalable Block-gram features, including 4-dimensional block features and 8-dimensional attribute features, are mined for the learning-based model training. Finally, feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model. In addition, six types of vulnerability labels are made on a dataset containing 33, 885 contracts, and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms, which show that the average detection latency speeds up 25x to 650x, compared with the features extracted by N-gram, and also can enhance the interpretability of the detection model.
关键词	Smart contract Bytecode & opcode Knowledgeable features Vulnerability detection Feature contribution
DOI	10.1016/j.dcan.2023.07.009
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation[62272248] ; Open Project Fund of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCHA202108] ; Open Project Fund of State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences[CARCH201905] ; Natural Science Foundation of Tianjin[20JCZDJC00610] ; Natural Science Foundation of Tianjin[2021KF0AB04]
WOS研究方向	Telecommunications
WOS类目	Telecommunications
WOS记录号	WOS:001447736200001
出版者	KEAI PUBLISHING LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40691
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Tao
作者单位	1.Tianjin Key Lab Network & Data Secur Technol, Tianjin, Peoples R China 2.Nankai Univ, Coll Comp Sci, Tianjin, Peoples R China 3.Key Lab Blockchain & Cyberspace Governance Zhejian, Hangzhou, Peoples R China 4.Chinese Acad Sci, State Key Lab Comp Architecture, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xie, Xueshuo,Wang, Haolong,Jian, Zhaolong,et al. Block-gram: Mining knowledgeable features for efficiently smart contract vulnerability detection[J]. DIGITAL COMMUNICATIONS AND NETWORKS,2025,11(1):1-12.
APA	Xie, Xueshuo,Wang, Haolong,Jian, Zhaolong,Fang, Yaozheng,Wang, Zichun,&Li, Tao.(2025).Block-gram: Mining knowledgeable features for efficiently smart contract vulnerability detection.DIGITAL COMMUNICATIONS AND NETWORKS,11(1),1-12.
MLA	Xie, Xueshuo,et al."Block-gram: Mining knowledgeable features for efficiently smart contract vulnerability detection".DIGITAL COMMUNICATIONS AND NETWORKS 11.1(2025):1-12.