EFEVD: Enhanced Feature Extraction for Smart Contract Vulnerability Detection
Authors: Chi Jiang, Xihan Liu, Shenao Wang, Jinzhuo Liu, Yin Zhang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results verify that the proposed approach significantly outperforms the state-of-the-art methods in terms of accuracy, recall, precision, and F1-score. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China 2Yunnan University {chijiang, xihanliu, shenaowang}@std.uestc.edu.cn, jinzhuo.liu@hotmail.com, zhangyin123@uestc.edu.cn |
| Pseudocode | No | No pseudocode or algorithm blocks are explicitly presented in the paper. |
| Open Source Code | Yes | Our data and code are available on Git Hub1. 1https://github.com/xawmx/MTLContractdetection |
| Open Datasets | No | The dataset used in this paper comes from a realworld smart contract dataset on Ethereum. The paper describes the dataset and its properties, but does not provide a specific link, DOI, repository, or formal citation for public access to the dataset itself. |
| Dataset Splits | No | The overall dataset is divided into a train set and a test set at a ratio of 7:3, where the train set is used for model training and the test set is used for model performance evaluations. A validation set is not explicitly mentioned. |
| Hardware Specification | Yes | All the models are trained using an NVIDIA Ge Force RTX 3060 GPU and implemented with Tensor Flow 1.15. |
| Software Dependencies | Yes | All the models are trained using an NVIDIA Ge Force RTX 3060 GPU and implemented with Tensor Flow 1.15. |
| Experiment Setup | Yes | The batch size is set to 64, and the learning rate is initialized to 0.01 and is dynamic during training. The epoch hyperparameter in this paper is set to 30. |