Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities until some predefined invocation depth. These tools require more search time as the invocation depth increases. Since the use of smart contracts increases rapidly, their analysis becomes difficult using these traditional tools. Recently, a machine learning technique called Long Short Term Memory (LSTM) has been used to predict the vulnerability of a smart contract. In the present article, we present how to classify smart contracts into Suicidal, Prodigal, Greedy, or Normal categories using Average Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), a variant of LSTM. We reduced the class imbalance by considering only distinct opcode combinations for normal contracts and achieved a weighted average F1 score of 90.0%. Such techniques can be utilized in real-time to analyze a large number of smart contracts and to improve their security.

智能合约的漏洞检测和安全性是不可改变的，因此至关重要。OYENTE和MAIAN等符号工具通常用于智能合约中的漏洞预测。由于这些工具的计算成本很高，因此通常将它们用于检测漏洞，直到达到某些预定义的调用深度为止。随着调用深度的增加，这些工具需要更多的搜索时间。由于智能合约的使用迅速增加，因此使用这些传统工具进行分析变得困难。最近，一种称为长短期记忆（LSTM）的机器学习技术已被用来预测智能合约的脆弱性。在本文中，我们将介绍如何将智能合约分为自杀，败家，贪婪，或使用平均随机梯度下降权重LSTM（AWD-LSTM）（LSTM的一种）的正常类别。通过仅考虑正常合同的不同操作码组合，我们减少了班级不平衡，并且加权平均F1分数达到90.0％。可以实时利用此类技术来分析大量智能合约并提高其安全性。