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Datasets:
pirocheto
/
phishing-url

Tasks:
Text Classification
Tabular Classification
Token Classification
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
phishing
url
security
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
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metadata
license: cc-by-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: test
        path: data/train.parquet
task_categories:
  - text-classification
  - tabular-classification
size_categories:
  - n<1K
annotations_creators:
  - found
tags:
  - phishing
  - url
  - security

Dataset Description

The provided dataset includes 11430 URLs with 87 extracted features.
The dataset are designed to be used as a benchmark for machine learning based phishing detection systems.
The datatset is balanced, it containes exactly 50% phishing and 50% legitimate URLs.

Features are from three different classes:

  • 56 extracted from the structure and syntax of URLs
  • 24 extracted from the content of their correspondent pages
  • 7 are extracetd by querying external services.

Details

Source Data

The diagram below illustrates the procedure for creating the corpus.
For details, please refer to the paper.

Diagram source data

Source: Extract form the paper

Load Dataset

  • With datasets:
from datasets import load_dataset

dataset = load_dataset("pirocheto/phishing-url")
  • With pandas and huggingface_hub:
import pandas as pd
from huggingface_hub import hf_hub_download

REPO_ID = "pirocheto/phishing-url"
FILENAME = "data.csv"

df = pd.read_csv(
    hf_hub_download(repo_id=REPO_ID, filename=FILENAME, repo_type="dataset")
)
  • With pandas only:
import pandas as pd

url = "https://huggingface.co/datasets/pirocheto/phishing-url/raw/main/data.csv"
df = pd.read_csv(url)

Citation

To give credit to the creators of this dataset, please use the following citation in your work:

  • BibTeX format
@article{Hannousse_2021,
   title={Towards benchmark datasets for machine learning based website phishing detection: An experimental study},
   volume={104},
   ISSN={0952-1976},
   url={http://dx.doi.org/10.1016/j.engappai.2021.104347},
   DOI={10.1016/j.engappai.2021.104347},
   journal={Engineering Applications of Artificial Intelligence},
   publisher={Elsevier BV},
   author={Hannousse, Abdelhakim and Yahiouche, Salima},
   year={2021},
   month=sep, pages={104347} }
  • APA format
Hannousse, A., & Yahiouche, S. (2021).
Towards benchmark datasets for machine learning based website phishing detection: An experimental study.
Engineering Applications of Artificial Intelligence, 104, 104347.