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Buiding our final year project on phishing website detection using machine learning

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phishing-website-detection-content-based

This is an End-to-End Machine Learning Project which focuses on phishing websites to classify phishing and legitimate ones. Particularly, We focused on content-based features like html tag based features. You can find feature extraction, data collection, preparation process here. Also, building ML models, evaluating them are available here.

inputs

  • csv files of phishing and legitimate URLs
    • verified_online.csv --> phishing websites URLs from phishtank.org
    • tranco_list.csv --> legitimate websites URLs from tranco-list.eu

general flow

  • Use csv file to get URLs
  • Send a request to each URL and receive a response by requests library of python
  • Use the content of response and parse it by BeautifulSoup module
  • Extract features and create a vector which contains numerical values for each feature
  • Repeat feature extraction process for all content\websites and create a structured dataframe
  • Add label at the end to the dataframes | 1 for phishing 0 for legitimate
  • Save the dataframe as csv and structured_data files are ready!
    • Check "structured_data_legitimate.csv" and "structured_data_phishing.csv" files.
  • After obtaining structured data, you can use combine them and use them as train and test data
  • You can split data as train and test like in the machine_learning.py first part, or you can implement K-fold cross-validation like in the second part of the same file. We implemented K-fold as K=5.
  • Then We implemented five different ML models:
    • Support Vector Machine
    • Gaussian Naive Bayes
    • Decision Tree
    • Random Forest
    • AdaBoost
  • You can obtain the confusion matrix, and performance measures: accuracy, precision, recall
  • Finally, We visualized the performance measures for all models.
    • Naive Bayes is the best for my case.

important notes

  • features are content-based and need BeautifulSoup module's methods and fields etc So, you should install it.

dataset

  • with your URL list, you can create your own dataset by using data_collector python file.

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