58 citations · 90 across the 9 of their papers we have counts for
5 papers · 1 filter
Schooling to Exploit Foolish Contracts
Tamer Abdelaziz, Aquinas Hobor
We introduce SCooLS, our Smart Contract Learning (Semi-supervised) engine. SCooLS uses neural networks to analyze Ethereum contract bytecode and identifies specific vulnerable func…
Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts
Tamer Abdelaziz, Aquinas Hobor
We introduce the Deep Learning Vulnerability Analyzer (DLVA) for Ethereum smart contracts based on neural networks. We train DLVA to judge bytecode even though the supervising orac…
Exploiting The Laws of Order in Smart Contracts
Aashish Kolluri, Ivica Nikolic, Ilya Sergey +2
We investigate a family of bugs in blockchain-based smart contracts, which we call event-ordering (or EO) bugs. These bugs are intimately related to the dynamic ordering of contrac…
BesFS: A POSIX Filesystem for Enclaves with a Mechanized Safety Proof
Shweta Shinde, Shengyi Wang, Pinghai Yuan +3
New trusted computing primitives such as Intel SGX have shown the feasibility of running user-level applications in enclaves on a commodity trusted processor without trusting a lar…
Finding The Greedy, Prodigal, and Suicidal Contracts at Scale
Ivica Nikolic, Aashish Kolluri, Ilya Sergey +2
Smart contracts---stateful executable objects hosted on blockchains like Ethereum---carry billions of dollars worth of coins and cannot be updated once deployed. We present a new s…