DeepPhish: Machine Learning Solutions for URL-Based Phishing Detection

 Abstract:
Phishing attacks have become one of the most prevalent cyber security threats, targeting individuals and organizations by tricking them into providing sensitive information through fraudulent websites. This project proposes an advanced phishing URL detection system utilizing machine learning techniques. By analyzing various features of URLs, such as lexical, host-based, and content-based attributes, the system effectively distinguishes between legitimate and malicious links. The model is trained using a dataset of phishing and legitimate URLs, and its performance is evaluated using metrics like accuracy, precision, recall, and F1-score.

Introduction:
With the increasing reliance on digital platforms for communication and transactions, phishing attacks have emerged as a critical cybersecurity challenge. Cybercriminals use deceptive techniques to lure users into clicking on malicious links, leading to data theft, financial loss, and system compromise. Traditional rule-based detection methods fail to keep up with the evolving nature of phishing attacks. Therefore, a machine learning-based approach offers a robust and adaptive solution to detect phishing URLs in real time.

Problem Statement:
Phishing attacks exploit users’ trust by masquerading as legitimate entities, leading to data breaches and identity theft. Existing methods rely heavily on blacklists, which are limited by their inability to detect newly created phishing websites. The primary challenge is to develop a proactive and efficient phishing URL detection system that can analyze and classify URLs with high accuracy.

Existing System and Disadvantages:

• Blacklist-based detection: Maintains a database of known phishing URLs but fails to detect new threats.
• Heuristic-based detection: Uses predefined rules but lacks adaptability to evolving phishing tactics.
• Browser security warnings: Depend on third-party services that may not be updated in real-time.
• Disadvantages:
  • High false negative rate.
  • Limited scalability and adaptability.
  • Inability to detect zero-day phishing attacks.

Proposed System and Advantages:

• Utilizes machine learning algorithms to analyze URL features and classify them as phishing or legitimate.
• Extracts lexical, host-based, and content-based features for a comprehensive evaluation.
• Implements supervised learning techniques such as Random Forest, Decision Tree, and Support Vector Machine (SVM) for classification.
• Advantages:
  • Higher detection accuracy.
  • Ability to detect zero-day phishing attacks.
  • Scalable and adaptable to new phishing techniques.
  • Reduces dependency on external blacklists.

Modules:

1. Data Collection Module: Gathers datasets containing phishing and legitimate URLs.
2. Feature Extraction Module: Extracts key attributes from URLs (lexical, host-based, and content-based).
3. Pre-processing Module: Cleans and prepares the dataset for training.
4. Model Training Module: Applies machine learning algorithms to train the model.
5. Evaluation Module: Assesses model performance using various metrics.
6. Detection and Prediction Module: Classifies incoming URLs as phishing or legitimate.
7. Deployment Module: Implements the model in a web application.

Algorithms:

• Gradient Boosting Classifier
• CatBoost Classifier
• XGBoost Classifier
• Multi-layer Perceptron
• Random Forest
• Support Vector Machine (SVM)
• Decision Tree
• K-Nearest Neighbors
• Logistic Regression
• Naive Bayes Classifier

Software and Hardware Requirements:

• Software:
  • Python
  • Jupyter Notebook
  • Scikit-learn
  • Pandas, NumPy
  • Flask (for deployment)
• Hardware:
  • Minimum 8GB RAM
  • Intel i5 or higher processor
  • GPU (optional for deep learning models)

Conclusion:
The proposed phishing URL detection system leverages machine learning techniques to provide a more efficient and accurate solution for identifying malicious links. By analyzing multiple features of URLs and training models on large datasets, the system surpasses traditional blacklist-based methods in detecting phishing websites. The results demonstrate high accuracy and reliability in real-time detection scenarios.

Future Enhancements:

• Implementing deep learning models for better generalization.
• Developing a browser extension for real-time phishing detection.
• Enhancing feature extraction by integrating Natural Language Processing (NLP) for webpage content analysis.
• Creating an adaptive system that updates its model dynamically based on new threats.