add new project #221

models/Phishing Website URL Detection by ML/Dataset/README.md

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+# Data Files
+
+This folder has the raw & extracted datafiles of this project. The description of each file is as follows:
+
+* [1.Benign_list_big_final.csv:](https://github.com/shreyagopal/Phishing-Website-Detection-by-Machine-Learning-Techniques/blob/master/DataFiles/1.Benign_list_big_final.csv) This file has list of legitimate urls. The total count is 35,300. The source of the dataset is University of New Brunswick, https://www.unb.ca/cic/datasets/url-2016.html. 
+
+* [2.online-valid.csv:](https://github.com/shreyagopal/Phishing-Website-Detection-by-Machine-Learning-Techniques/blob/master/DataFiles/2.online-valid.csv) This file is downloaded from the opensource service called PhishTank. This service provide a set of phishing URLs in multiple formats like csv, json etc. that gets updated hourly. To download the latest data: https://www.phishtank.com/developer_info.php.
+
+* [3.legitimate.csv:](https://github.com/shreyagopal/Phishing-Website-Detection-by-Machine-Learning-Techniques/blob/master/DataFiles/3.legitimate.csv) This file has the extracted features of the 5000 legitimate URLs which are randonmly selected from the '1.Benign_list_big_final.csv' file.
+
+* [4.phishing.csv](https://github.com/shreyagopal/Phishing-Website-Detection-by-Machine-Learning-Techniques/blob/master/DataFiles/4.phishing.csv) This file has the extracted features of the 5000 phishing URLs which are randonmly selected from the '2.online-valid.csv' file.
+
+* [5.urldata.csv](https://github.com/shreyagopal/Phishing-Website-Detection-by-Machine-Learning-Techniques/blob/master/DataFiles/5.urldata.csv) This file is nothing but a combination of the above two files. It contains extracted features of 10,000 URLs both legitimate & phishing.

models/Phishing Website URL Detection by ML/README.md

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+# Phishing Url  Detection by Machine Learning Techniques
+
+## Objective
+A phishing url is a common social engineering method that mimics trustful uniform resource locators (URLs) and webpages. The objective of this project is to train machine learning models and deep neural nets on the dataset created to predict phishing websites. Both phishing and benign URLs of websites are gathered to form a dataset and from them required URL and website content-based features are extracted. The performance level of each model is measures and compared.
+
+## Data Collection
+The set of phishing URLs are collected from opensource service called **PhishTank**. This service provide a set of phishing URLs in multiple formats like csv, json etc. that gets updated hourly.  From this dataset, 5000 random phishing URLs are collected to train the ML models.
+
+ This dataset has a collection of benign, spam, phishing, malware & defacement URLs. Out of all these types, the benign url dataset is considered for this project. From this dataset, 5000 random legitimate URLs are collected to train the ML models.
+.
+
+## Feature Extraction
+The below mentioned category of features are extracted from the URL data:
+
+1.   Address Bar based Features <br>
+          &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In this category 9 features are extracted.
+2.   Domain based Features<br>
+          &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;In this category 4 features are extracted.
+
+## Models & Training
+
+Before stating the ML model training, the data is split into 80-20 i.e., 8000 training samples & 2000 testing samples. From the dataset, it is clear that this is a supervised machine learning task. There are two major types of supervised machine learning problems, called classification and regression.
+
+This data set comes under classification problem, as the input URL is classified as phishing (1) or legitimate (0). The supervised machine learning models (classification) considered to train the dataset in this project are:
+
+* Decision Tree
+* Random Forest
+* Multilayer Perceptrons
+* XGBoost
+* Autoencoder Neural Network
+* Support Vector Machines

models/Phishing Website URL Detection by ML/model.py

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+# model.py
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, accuracy_score
+import joblib
+
+def train_model(data_path):
+    # Load the dataset
+    data = pd.read_csv(data_path)
+
+    # Split the data into features and target
+    X = data.drop(columns=['Label'])  # Assuming 'Label' is the target column
+    y = data['Label']
+
+    # Split the dataset into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Initialize the model
+    model = RandomForestClassifier(n_estimators=100, random_state=42)
+
+    # Train the model
+    model.fit(X_train, y_train)
+
+    # Make predictions
+    y_pred = model.predict(X_test)
+
+    # Evaluate the model
+    print("Accuracy:", accuracy_score(y_test, y_pred))
+    print(classification_report(y_test, y_pred))
+
+    # Save the model as a .pkl file
+    joblib.dump(model, 'phishing_model.pkl')
+
+if __name__ == "__main__":
+    train_model('data.csv')  # Replace with your dataset path