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Found 4 papers, 1 papers with code
Schooling to Exploit Foolish Contracts
SCooLS uses neural networks to analyze Ethereum contract bytecode and identifies specific vulnerable functions.
Smart Learning to Find Dumb Contracts (Extended Version)
Second, Sibling Detector (SD) classifies contracts when a target contract's vector is Euclidian-close to a labeled contract's vector in a training set; although only able to judge 55. 7% of the contracts in our test set, it has a Slither-predictive accuracy of 97. 4% with a false positive rate of only 0. 1%.
Finding The Greedy, Prodigal, and Suicidal Contracts at Scale
On a subset of3, 759 contracts which we sampled for concrete validation and manual analysis, we reproduce real exploits at a true positive rate of 89%, yielding exploits for3, 686 contracts.
Cryptography and Security
A Concurrent Perspective on Smart Contracts
In this paper, we explore remarkable similarities between multi-transactional behaviors of smart contracts in cryptocurrencies such as Ethereum and classical problems of shared-memory concurrency.
Distributed, Parallel, and Cluster Computing
