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Smart contract is the building block of blockchain systems that enablesautomated peer-to-peer transactions and decentralized services. With theincreasing popularity of smart contracts, blockchain systems, in particularEthereum, have been the "paradise" of versatile fraud activities in whichPonzi, Honeypot and Phishing are the prominent ones. Formal verification andsymbolic analysis have been employed to combat these destructive scams byanalyzing the codes and function calls, yet the vulnerability of each\emph{individual} scam should be predefined discreetly. In this work, wepresent SCSGuard, a novel deep learning scam detection framework that harnessesthe automatically extractable bytecodes of smart contracts as their newfeatures. We design a GRU network with attention mechanism to learn from the\emph{N-gram bytecode} patterns, and determines whether a smart contract isfraudulent or not. Our framework is advantageous over the baseline algorithmsin three aspects. Firstly, SCSGuard provides a unified solution to differentscam genres, thus relieving the need of code analysis skills. Secondly, theinference of SCSGuard is faster than the code analysis by several order ofmagnitudes. Thirdly, experimental results manifest that SCSGuard achieves highaccuracy (0.92$\sim$0.94), precision (0.94$\sim$0.96\%) and recall(0.97$\sim$0.98) for both Ponzi and Honeypot scams under similar settings, andis potentially useful to detect new Phishing smart contracts.

SCSGuard: Deep Scam Detection for Ethereum Smart Contracts