TY - JOUR
T1 - VULPEDIA
T2 - Detecting vulnerable ethereum smart contracts via abstracted vulnerability signatures
AU - Ye, Jiaming
AU - Ma, Mingliang
AU - Lin, Yun
AU - Ma, Lei
AU - Xue, Yinxing
AU - Zhao, Jianjun
N1 - Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/10
Y1 - 2022/10
N2 - Recent years have seen smart contracts are getting increasingly popular in building trustworthy decentralized applications. Previous research has proposed static and dynamic techniques to detect vulnerabilities in smart contracts. These tools check vulnerable contracts against several predefined rules. However, the emerging new vulnerable types and programming skills to prevent possible vulnerabilities emerging lead to a large number of false positive and false negative reports of tools. To address this, we propose VULPEDIA, which mines expressive vulnerability signatures from contracts. VULPEDIA is based on the relaxed assumption that the owner of contract is not malicious. Specifically, we extract structural program features from vulnerable and benign contracts as vulnerability signatures, and construct a systematic detection method based on detection rules composed of vulnerability signatures. Compared with the rules defined by state-of-the-arts, our approach can extract more expressive rules to achieve better completeness (i.e., detection recall) and soundness (i.e., precision). We further evaluate VULPEDIA with four baselines (i.e., Slither, Securify, SmartCheck and Oyente) on the testing dataset consisting of 17,770 contracts. The experiment results show that VULPEDIA achieves best performance of precision on 4 types of vulnerabilities and leading recall on 3 types of vulnerabilities meanwhile exhibiting the great efficiency performance.
AB - Recent years have seen smart contracts are getting increasingly popular in building trustworthy decentralized applications. Previous research has proposed static and dynamic techniques to detect vulnerabilities in smart contracts. These tools check vulnerable contracts against several predefined rules. However, the emerging new vulnerable types and programming skills to prevent possible vulnerabilities emerging lead to a large number of false positive and false negative reports of tools. To address this, we propose VULPEDIA, which mines expressive vulnerability signatures from contracts. VULPEDIA is based on the relaxed assumption that the owner of contract is not malicious. Specifically, we extract structural program features from vulnerable and benign contracts as vulnerability signatures, and construct a systematic detection method based on detection rules composed of vulnerability signatures. Compared with the rules defined by state-of-the-arts, our approach can extract more expressive rules to achieve better completeness (i.e., detection recall) and soundness (i.e., precision). We further evaluate VULPEDIA with four baselines (i.e., Slither, Securify, SmartCheck and Oyente) on the testing dataset consisting of 17,770 contracts. The experiment results show that VULPEDIA achieves best performance of precision on 4 types of vulnerabilities and leading recall on 3 types of vulnerabilities meanwhile exhibiting the great efficiency performance.
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U2 - 10.1016/j.jss.2022.111410
DO - 10.1016/j.jss.2022.111410
M3 - Article
AN - SCOPUS:85133492932
VL - 192
JO - Journal of Systems and Software
JF - Journal of Systems and Software
SN - 0164-1212
M1 - 111410
ER -