Making Smart Contracts Smarter

By Loi Luu, Duc-Hiep Chu, Hrishi Olickel, Prateek Saxena, Aquinas Hobor. IACR Cryptology ePrint Archive, 2016

2,368 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/7968129a609364598baefbc35249400959406252

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Citations in the corpus

Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends (2019, background): “Among 19,366 smart contracts in Ethereum, Oyente flagged 8,833 of them vulnerable, including the The DAO [40].”
Survey on blockchain based smart contracts: Applications, opportunities and challenges (2021, methodology): “Identification of such vulnerabilities save money, time and safeguards the entire blockchain ecosystem • Security analysis [242], [243], [244], [245], [246], [247] • Vulnerability identification [248], [249] • Automated security testing [250], [251] , [252] • Symbolic analysis [253] • Security bug i”
A Semantic Framework for the Security Analysis of Ethereum smart contracts (2018, methodology): “This bug is in the literature classified as reentrancy bug [13,21] as the affected contract was drained out of money by subsequently reentering it and performing transactions to the attacker on behalf of the contract.”
An Overview on Smart Contracts: Challenges, Advances and Platforms (2019, positive): “In particular, OYENTE was proposed in [46] to identify potential security bugs including mishandled exceptions and timestamp-dependent problems.”
An Overview of Smart Contract: Architecture, Applications, and Future Trends (2018, methodology): “interface, which are respectively used to implement contract deployment, transaction processing and transaction inquiries [4], [24].”
Blockchain-based framework for supply chain traceability: A case example of textile and clothing industry (2021, background): “Each transaction is verified against the rules before recording on the shared ledger (Luu et al., 2016).”
Smart Contract Development: Challenges and Opportunities (2021, background): “Cryptocurrencies which are based on blockchain have attracted considerable attention lately [90].”
Osiris: Hunting for Integer Bugs in Ethereum Smart Contracts (2018, methodology): “In this section we assess the correctness and effectiveness of Osiris via an empirical analysis and demonstrate its usefulness in detecting real-world vulnerabilities in Ethereum smart contracts.”
Erays: Reverse Engineering Ethereum's Opaque Smart Contracts (2018, methodology): “There are several symbolic execution engines for Ethereum smart contracts, including Oyente [31], Manticore [4], and Mythril [5].”
Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts (2019, methodology): “Symbolic execution in the Ethereum space is much less widely explored, but a few other tools do exist, such as Mythril Classic [2], teEther [10], Oyente [11] and Maian [12].”
MadMax: surviving out-of-gas conditions in Ethereum smart contracts (2018, background): “Systems including Oyente [Luu et al. 2016a], MAIAN [Nikolic et al.”
KEVM: A Complete Formal Semantics of the Ethereum Virtual Machine (2018, methodology): “In fact, many classes of subtle bugs exist in smart contracts, ranging from transaction-ordering dependencies [18] to mishandled exceptions [18].”
A Survey of Attacks on Ethereum Smart Contracts (SoK) (2017, methodology): “For instance, [43] reports that ∼ 28% of the analyzed contracts potentially contain “exception disorder” vulnerabilities.”
A Survey on the Security of Blockchain Systems (2017, methodology): “discover that 8,833 out of 19,366 existing Ethereum contracts are vulnerable [17].”
Blockchain technology: Is it hype or real in the construction industry? (2020, background): “Famous permissionless blockchains such as Bitcoin and Ethereum use proof of work (PoW) consensus algorithms and however, Ethereum is planning to move to a proof of stake (PoS) consensus algorithm [11, 40].”