SmartCheck: Static Analysis of Ethereum Smart Contracts

By Sergei Tikhomirov, Ekaterina Voskresenskaya, Ivan Ivanitskiy, Ramil Takhaviev, Evgeny Marchenko, YA Alexandrov. International Workshop on Emerging Trends in Software Engineering for Blockchain, 2018

839 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8f22bf55536b50145bb117c97e13ea4b32a5e8fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhenguang Liu
2. Marcus Pendleton
3. Tharaka Mawanane Hewa
4. Ala Al-Fuqaha
5. Baowen Xu
6. Xin Xia
7. Laurent L. Njilla
8. Rui Abreu

In Information Systems

1. Zhenguang Liu
2. Xiang Wang
3. Marcus Pendleton
4. Tharaka Mawanane Hewa
5. Ala Al-Fuqaha
6. Baowen Xu
7. Xin Xia
8. Laurent L. Njilla

In Signal Processing

1. Tharaka Mawanane Hewa
2. Ala Al-Fuqaha
3. Xin Xia
4. Rui Abreu
5. Madhusanka Liyanage
6. Mika E. Ylianttila
7. Khaled Hamed Salah
8. Yuan Zhuang

Citations in the corpus

• Smart Contract Development: Challenges and Opportunities (2021, methodology): “developed SmartCheck to detect potential problems by checking against XPath patterns [142].”
• Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact (2017, methodology): “To counteract these vulnerabilities, researchers have started to develop tools for automatically analysing Ethereum contracts [23, 30, 31, 32, 33].”
• Survey on blockchain based smart contracts: Applications, opportunities and challenges (2021, background): “The results conveyed that SmartCheck [251] is statistically more effective in automated security testing than other tools evaluated.”
• Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection (2021, methodology): “We empirically checked the Vulnerable contract using the state-of-the-art tools including Oyente [8], Securify [18], Smartcheck [16], Slither [53], and Mythril [52], and manually inspected their generated reports.”
• Sereum: Protecting Existing Smart Contracts Against Re-Entrancy Attacks (2018, methodology): “SmartCheck [40] first converts the solidity contract source code to a XML-based parse-tree and then searches for vulnerable patterns through XPath queries.”
• Smart Contract Vulnerability Detection using Graph Neural Network (2020, methodology): “First, we compare our DR-GCN and TMP methods with state-of-the-art smart contract vulnerability detection meth-ods, namely Oyente [Luu et al. , 2016], Mythril [Mueller, 2017], Smartcheck [Tikhomirov et al. , 2018], and Securify [Tsankov et al. , 2018], on the reentrancy vulnerability detection task.”
• A Survey on Ethereum Systems Security (2019, background): “[155] (32) propose SmartCheck to detect vulnerabilities in Solidity contracts by translating Solidity source code into an XML-based parse-tree and checks it against specific XPath patterns; they can detect 10 types of vulnerabilities, such as the DoS with unexpected revert vulnerability (5), the m”
• Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts (2019, methodology): “/A 23 Remix https://github.com/ethereum/remix 24 SASC [43] N/A 25 sCompile [6] N/A 26 Securify [42] https://github.com/eth-sri/securify 27 Slither [16] https://github.com/crytic/slither 28 Smartcheck [39] https://github.com/smartdec/smartcheck 29 Solgraph https://github.com/raineorshine/solgraph 30 ”
• Blockchain for AI: Review and Open Research Challenges (2019, background): “a critical importance, and some tools have been developed to assess the security state of a smart contract code [134]–[136].”