  With blockchain technology rapidly progress, the smart contracts have become
a common tool in a number of industries including finance, healthcare,
insurance and gaming. The number of smart contracts has multiplied, and at the
same time, the security of smart contracts has drawn considerable attention due
to the monetary losses brought on by smart contract vulnerabilities. Existing
analysis techniques are capable of identifying a large number of smart contract
security flaws, but they rely too much on rigid criteria established by
specialists, where the detection process takes much longer as the complexity of
the smart contract rises. In this paper, we propose HyMo as a multi-modal
hybrid deep learning model, which intelligently considers various input
representations to consider multimodality and FastText word embedding
technique, which represents each word as an n-gram of characters with BiGRU
deep learning technique, as a sequence processing model that consists of two
GRUs to achieve higher accuracy in smart contract vulnerability detection. The
model gathers features using various deep learning models to identify the smart
contract vulnerabilities. Through a series of studies on the currently publicly
accessible dataset such as ScrawlD, we show that our hybrid HyMo model has
excellent smart contract vulnerability detection performance. Therefore, HyMo
performs better detection of smart contract vulnerabilities against other
approaches.
