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      The Art of The Scam: Demystifying Honeypots in Ethereum Smart Contracts

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          Abstract

          Modern blockchains, such as Ethereum, enable the execution of so-called smart contracts - programs that are executed across the decentralised blockchain network. As smart contracts become more popular and carry more value, they become more of an interesting target for attackers. In the past few years, several smart contracts have been found to be vulnerable and thus exploited by attackers. However, a new trend towards a more proactive approach seems to be on the rise where attackers do not search for vulnerable contracts anymore. Instead, they try to lure their victims into traps by deploying vulnerable-looking contracts that contain hidden traps. This type of contracts is commonly referred to as honeypots. In this paper, we present the first systematic analysis of honeypots, by investigating their prevalence, behaviour and impact on the Ethereum blockchain. We develop a taxonomy of honeypot techniques and use this to build HONEYBADGER - a tool that employs symbolic execution and well defined heuristics to expose smart contract honeypots. We perform a large-scale analysis of more than 2 million smart contracts and show that our tool not only achieves high precision, but also high scalability. We identify 690 honeypots as well as 240 victims in the wild, with an accumulated profit of more than $90,000 for the honeypot creators. Our manual validation shows that 87% of the reported contracts are indeed honeypots.

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          Most cited references12

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          Making Smart Contracts Smarter

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            A Survey of Attacks on Ethereum Smart Contracts (SoK)

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              A normalized Levenshtein distance metric.

              Although a number of normalized edit distances presented so far may offer good performance in some applications, none of them can be regarded as a genuine metric between strings because they do not satisfy the triangle inequality. Given two strings X and Y over a finite alphabet, this paper defines a new normalized edit distance between X and Y as a simple function of their lengths (|X| and |Y|) and the Generalized Levenshtein Distance (GLD) between them. The new distance can be easily computed through GLD with a complexity of O(|X|.|Y|) and it is a metric valued in [0, 1] under the condition that the weight function is a metric over the set of elementary edit operations with all costs of insertions/deletions having the same weight. Experiments using the AESA algorithm in handwritten digit recognition show that the new distance can generally provide similar results to some other normalized edit distances and may perform slightly better if the triangle inequality is violated in a particular data set.
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                Author and article information

                Journal
                19 February 2019
                Article
                1902.06976
                d2f7165f-f49f-481d-bfbf-427a97f43b27

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.CR

                Security & Cryptology
                Security & Cryptology

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