README.md

metadata

license: cc-by-4.0
task_categories:
  - text-classification
  - feature-extraction
  - tabular-classification
language:
  - af
  - en
  - et
  - sw
  - sv
  - sq
  - de
  - ca
  - hu
  - da
  - tl
  - so
  - fi
  - fr
  - cs
  - hr
  - cy
  - es
  - sl
  - tr
  - pl
  - pt
  - nl
  - id
  - sk
  - lt
  - 'no'
  - lv
  - vi
  - it
  - ro
  - ru
  - mk
  - bg
  - th
  - ja
  - ko
  - multilingual
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        pattern:
          - phishing_features_train.csv
          - phishing_url_train.csv
      - split: test
        pattern:
          - phishing_features_val.csv
          - phishing_url_val.csv

I have decided to release the auto-moderation models all at once sometime in July/August, 2023. The datasets for training these models will be avaliable first.

The features dataset is original, and my feature extraction method is covered in feature_extraction.py

In the features dataset, there're 911,180 websites online at the time of data collection. The plots below show the regression line and correlation coefficients of 22+ features extracted and whether the URL is malicious. If we could plot the lifespan of URLs, we could see that the oldest website has been online since Nov 7th, 2008, while the most recent phishing websites appeared as late as July 10th, 2023.

Here are two images showing the correlation coefficient and correlation of determination between predictor values and the target value is_malicious.

Let's exmain the correlations one by one and cross out any unreasonable or insignificant correlations.

Variable	Justification for Crossing Out
redirects	contracdicts previous research (as redirects increase, is_malicious tends to decrease by a little)
not_indexed_by_google	0.00 correlation
email_submission	contracdicts previous research
request_url_percentage
url_anchor_percentage	contracdicts previous research
meta_percentage	0.00 correlation
script_percentage
link_percentage
mouseover_changes	contracdicts previous research & 0.00 correlation
right_clicked_disabled	contracdicts previous research & 0.00 correlation
popup_window_has_text_field	contracdicts previous research
use_iframe	contracdicts previous research
has_suspicious_ports	contracdicts previous research
external_favicons	contracdicts previous research
TTL (Time to Live)
ip_address_count
TXT_record	all websites had a TXT record
check_sfh	contracdicts previous research
count_domain_occurances
domain_registration_length
abnormal_URL
age_of_domain
page_rank_decimal

Pre-training Ideas

For training, I split the classification task into two stages in anticipation of the limited availability of online phishing websites due to their short lifespan, as well as the possibility that research done on phishing is not up-to-date:

1. a small multilingual BERT model to output the confidence level of a URL being malicious to model #2, by finetuning on 2,436,727 legitimate and malicious URLs
2. (probably) LightGBM to analyze the confidence level, along with roughly 19 extracted features

This way, I can make the most out of the limited phishing websites avaliable.

Source of the URLs

• https://moz.com/top500
• https://phishtank.org/phish_search.php?valid=y&active=y&Search=Search
• https://www.kaggle.com/datasets/siddharthkumar25/malicious-and-benign-urls
• https://www.kaggle.com/datasets/sid321axn/malicious-urls-dataset
• https://github.com/ESDAUNG/PhishDataset
• https://github.com/JPCERTCC/phishurl-list
• https://github.com/Dogino/Discord-Phishing-URLs

Reference

• https://www.kaggle.com/datasets/akashkr/phishing-website-dataset
• https://www.kaggle.com/datasets/shashwatwork/web-page-phishing-detection-dataset
• https://www.kaggle.com/datasets/aman9d/phishing-data

Side notes

• Cloudflare offers an API for URL scanning, with a generous global rate limit of 1200 requests every 5 minutes.