ReGuard: Finding Reentrancy Bugs in Smart Contracts

By Chao Liu, Han Liu, Zhao Cao, Zhong Chen, Bangdao Chen, B. Roscoe. 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion), 2018

377 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0b0381cfd895fd5aafe373f8a263614ec9cf031f

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Citations in the corpus

Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection (2021, background): “Similarly, [28] develops a fuzzing-based analyzer to identify the reentrancy vulnerability.”
Smart Contract Development: Challenges and Opportunities (2021, methodology): “developed ReGuard to detect reentrancy bugs through fuzz testing [87].”
Blockchain smart contracts: Applications, challenges, and future trends (2021, methodology): “Some other work focused on specific vulnerabilities, such as ReGuard [54] to detect re-entrancy bugs and EthRacer [48] to detect event-ordering bugs.”
Survey on blockchain based smart contracts: Applications, opportunities and challenges (2021, methodology): “[254] presented the tool ReGuard, which are usable to identify reentrency bugs in smart contracts.”
The Roadmap to 6G Security and Privacy (2021, neutral): “The accurate functionality of smart contacts can be checked by identifying semantic flaws [101], [102], using security check tools [103], [104], [105] and performing formal verification [106]–[109].”
Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts (2019, neutral): “tps://github.com/christoftorres/Osiris 19 Oyente [27] https://github.com/melonproject/oyente 20 Porosity [38] https://github.com/comaeio/porosity 21 rattle https://github.com/crytic/rattle 22 ReGuard [25] N/A 23 Remix https://github.com/ethereum/remix 24 SASC [43] N/A 25 sCompile [6] N/A 26 Securify”
Smart Contract Vulnerability Detection using Graph Neural Network (2020, methodology): “With remarkable success of neural networks in various ﬁelds [Cheng et al. , 2019] [Liu et al. , 2018a], graph neural network has received increasing attention.”
An Overview on Smart Contracts: Challenges, Advances and Platforms (2019, positive): “[40] proposed to perform the fuzz testing on smart contracts by iteratively generating random but diverse transactions to detect reentrancy bugs.”
How should we understand the digital economy in Asia? Critical assessment and research agenda (2020, background): “Other authors have paid particular attention to vulnerability issues, with research focused on repeated payments related to the same contract – the “re-entrancy at-tack” problem (Liu et al., 2018).”