# Multi-Class classification of vulnerabilities in Smart Contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing

arXiv: https://arxiv.org/abs/2004.00362 · HTML: https://mlsanity.com/paper/2004.00362/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning](https://mlsanity.com/paper/2103.12607/) (2021)
- [An Automated Vulnerability Detection Framework for Smart Contracts](https://mlsanity.com/paper/2301.08824/) (2023)
- [Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis](https://mlsanity.com/paper/2407.18639/) (2024)
- [Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities](https://mlsanity.com/paper/2409.10574/) (2024)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Vulnerability Detection in Smart Contracts: A Comprehensive Survey](https://mlsanity.com/paper/2407.07922/) (2024)
- [Knowledge Migration Framework for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2412.11175/) (2024)

## Abstract (arXiv)

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 number of smart contracts is increasing exponentially, it is difficult to analyze the contracts using these traditional tools. Recently a machine learning technique called Long Short Term Memory (LSTM) has been used for binary classification, i.e., to predict whether a smart contract is vulnerable or not. This technique requires nearly constant search time as the invocation depth increases. In the present article, we have shown a multi-class classification, where we classify a smart contract in Suicidal, Prodigal, Greedy, or Normal categories. We used Average Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), which is a variant of LSTM, to perform classification. We reduced the class imbalance (a large number of normal contracts as compared to other categories) by considering only the distinct opcode combination for normal contracts. We have achieved a weighted average Fbeta score of 90.0%. Hence, such techniques can be used to analyze a large number of smart contracts and help to improve the security of these contracts.