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Detect-phishing-websites-using-ML

This project is a simple example which trains the model to predict phishing websites. Phishing websites are fake websites which try to gain the trust of users to steal private data of users.

  • Best accuracy score - 97.0% using Random forest method
  • Worst accuract score - 48.5% using One class svm method

Requirements

  • Scikit-learn (sklearn)
  • Numpy

Requirements can be installed by executing pip install -r requirements.txt

Data set

The data set for training has been taken from UCI archive

Execution

  • python classifier.py to check the accuracy of the script.
  • python classifier.py google.com to check whether google.com is phishing website or not.

Parameters in dataset

Each value in the dataset contains all these elements and all are seperated by a comma.

  1. having_IP_Address { -1,1 }
  2. URL_Length { 1,0,-1 }
  3. Shortining_Service { 1,-1 }
  4. having_At_Symbol { 1,-1 }
  5. double_slash_redirecting { -1,1 }
  6. Prefix_Suffix { -1,1 }
  7. having_Sub_Domain { -1,0,1 }
  8. SSLfinal_State { -1,1,0 }
  9. Domain_registeration_length { -1,1 }
  10. Favicon { 1,-1 }
  11. port { 1,-1 }
  12. HTTPS_token { -1,1 }
  13. Request_URL { 1,-1 }
  14. URL_of_Anchor { -1,0,1 }
  15. Links_in_tags { 1,-1,0 }
  16. SFH { -1,1,0 }
  17. Submitting_to_email { -1,1 }
  18. Abnormal_URL { -1,1 }
  19. Redirect { 0,1 }
  20. on_mouseover { 1,-1 }
  21. RightClick { 1,-1 }
  22. popUpWidnow { 1,-1 }
  23. Iframe { 1,-1 }
  24. age_of_domain { -1,1 }
  25. DNSRecord { -1,1 }
  26. web_traffic { -1,0,1 }
  27. Page_Rank { -1,1 }
  28. Google_Index { 1,-1 }
  29. Links_pointing_to_page { 1,0,-1 }
  30. Statistical_report { -1,1 }
  31. Result { -1,1 }

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