Cybersecurity Datasets:
20. Vulnerable Smart Contracts (BCCC-VulSCs-2023)

A vulnerable smart contract refers to a smart contract in blockchain technology that has security flaws or weaknesses in its code, making it susceptible to attacks or misuse. Smart contracts are self-executing contracts with the terms of the agreement directly written into lines of code. They run on blockchain platforms like Ethereum and are used to automate the execution of an agreement so that all participants can be immediately certain of the outcome, without any intermediary's involvement or time loss.

The BCCC-VulSCs-2023 dataset contains a collection of 70 different attributes related to Solidity-based smart contracts. These attributes are instrumental in evaluating the vulnerable or secure Smart Contracts. Presented here are statistical summaries - average, minimum, maximum, and standard deviation - for each of these non-binary features in the dataset.

Figure 1: Average

Figure 2: Minimum

Figure 3: Maximum

Figure 4: Standard deviation

Dataset Details
The dataset is imbalanced with it being made up by 73.39% secure Smart Contracts and 26.60% vulnerable Smart Contracts. The distribution of labels is shown in the Figure 5 below. The dataset contains a total of 36670 records with 26,914 secure and 9756 vulnerable Smart Contracts. Table 1, shows list and description of the extracted features using SCsVolLyzer-V1.0.

Figure 5: Labels distribution

Table 1: Extracted Features

You may redistribute, republish, and mirror the BCCC-VolSCs-2023 dataset in any form. However, any use or redistribution of data must include a citation to the BCCC-VulSCs-2023 dataset and the following paper:

- Sepideh Hajihosseinkhani, Arash Habibi Lashkari, Ali Mizani, “Unveiling Vulnerable Smart Contracts: Toward Profiling Vulnerable Smart Contracts using Genetic Algorithm and Generating Benchmark Dataset”, Blockchain: Research and Applications, Vol. 4, December 2023

You can download this dataset from here.
Researchers named among top researchers for Canada 150
The cybersecurity Research and Academic Leadership award, Canada 2019
The cybersecurity academic award, Canada 2017