  Smart contract is the building block of blockchain systems that enables
automated peer-to-peer transactions and decentralized services. With the
increasing popularity of smart contracts, blockchain systems, in particular
Ethereum, have been the "paradise" of versatile fraud activities in which
Ponzi, Honeypot and Phishing are the prominent ones. Formal verification and
symbolic analysis have been employed to combat these destructive scams by
analyzing the codes and function calls, yet the vulnerability of each
\emph{individual} scam should be predefined discreetly. In this work, we
present SCSGuard, a novel deep learning scam detection framework that harnesses
the automatically extractable bytecodes of smart contracts as their new
features. We design a GRU network with attention mechanism to learn from the
\emph{N-gram bytecode} patterns, and determines whether a smart contract is
fraudulent or not. Our framework is advantageous over the baseline algorithms
in three aspects. Firstly, SCSGuard provides a unified solution to different
scam genres, thus relieving the need of code analysis skills. Secondly, the
inference of SCSGuard is faster than the code analysis by several order of
magnitudes. Thirdly, experimental results manifest that SCSGuard achieves high
accuracy (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, and
is potentially useful to detect new Phishing smart contracts.
