Phishing website detection an amazing project

This experiment was created using machine learning algorithms with turicreate.

Data set in this experiment was taken from the 'Machine Learning Repository'.

Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Phishing+Websites

turicreate: https://github.com/apple/turicreate

Accuracy Results

1. Boosted Decision Tree = 0.913~ (Lowest)
2. Decision Tree = 0.926~
3. K Nearest Neighbour = 0.939~ (Highest)
4. Logistic Regression = 0.928~

Script

  $ python script.py
  
  [1] Boosted decision tree
  [2] Decision tree
  [3] Nearest neighbour
  [4] Logistic regression
  
  Select ML Algorithm: 1
  
  Finished parsing file dataset.csv
  Parsing completed. Parsed 100 lines in 0.041388 secs.
  ------------------------------------------------------
  Inferred types from first 100 line(s) of file as
  column_type_hints=[int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int,int]
  If parsing fails due to incorrect types, you can correct
  the inferred type list above and pass it to read_csv in
  the column_type_hints argument
  ------------------------------------------------------
  Finished parsing file dataset.csv
  Parsing completed. Parsed 11055 lines in 0.035361 secs.
  PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.
            You can set ``validation_set=None`` to disable validation tracking.
  
  Boosted trees classifier:
  --------------------------------------------------------
  Number of examples          : 8365
  Number of classes           : 2
  Number of feature columns   : 30
  Number of unpacked features : 30
  +-----------+--------------+-------------------+---------------------+-------------------+---------------------+
  | Iteration | Elapsed Time | Training-accuracy | Validation-accuracy | Training-log_loss | Validation-log_loss |
  +-----------+--------------+-------------------+---------------------+-------------------+---------------------+
  | 1         | 0.012667     | 0.909265          | 0.892019            | 0.505161          | 0.509974            |
  | 2         | 0.019960     | 0.917753          | 0.908451            | 0.401738          | 0.406688            |
  +-----------+--------------+-------------------+---------------------+-------------------+---------------------+
  
  
  >>> Accuracy         : 0.9165194346289752

Usage

$ git clone https://github.com/danrevah/detect-phishing-websites.git
$ cd detect-phishing-websites
$ pip install virtualenv
$ source venv/bin/activate
$ pip install -r requirements.txt
$ python script.py

DataSet

Based on the following attributes:

• having_IP_Address { -1,1 }
• URL_Length { 1,0,-1 }
• Shortining_Service { 1,-1 }
• having_At_Symbol { 1,-1 }
• double_slash_redirecting { -1,1 }
• Prefix_Suffix { -1,1 }
• having_Sub_Domain { -1,0,1 }
• SSLfinal_State { -1,1,0 }
• Domain_registeration_length { -1,1 }
• Favicon { 1,-1 }
• port { 1,-1 }
• HTTPS_token { -1,1 }
• Request_URL { 1,-1 }
• URL_of_Anchor { -1,0,1 }
• Links_in_tags { 1,-1,0 }
• SFH { -1,1,0 }
• Submitting_to_email { -1,1 }
• Abnormal_URL { -1,1 }
• Redirect { 0,1 }
• on_mouseover { 1,-1 }
• RightClick { 1,-1 }
• popUpWidnow { 1,-1 }
• Iframe { 1,-1 }
• age_of_domain { -1,1 }
• DNSRecord { -1,1 }
• web_traffic { -1,0,1 }
• Page_Rank { -1,1 }
• Google_Index { 1,-1 }
• Links_pointing_to_page { 1,0,-1 }
• Statistical_report { -1,1 }
• Result { -1,1 }