PhishNet: Detecting Phishing URLs Using Convolutional Neural Networks

Abstract:

Phishing is a form of cyber-attack in which attackers deceive users into providing sensitive information by disguising malicious websites as legitimate ones. Traditional phishing detection methods rely on heuristic and rule-based approaches, which are often ineffective against evolving threats. This project proposes a Convolutional Neural Network (CNN)-based phishing URL detection system that can classify URLs as either legitimate or phishing with high accuracy. The model learns patterns from URL structures and domain-related features to detect phishing attempts effectively. The proposed system provides an automated and intelligent approach to phishing detection, reducing reliance on manual intervention and enhancing cyber security.

Introduction

Phishing attacks have become one of the most prevalent cybersecurity threats, leading to financial losses and data breaches. Attackers create fraudulent websites that mimic legitimate ones to steal login credentials, personal data, and financial details. Conventional methods such as blacklists and heuristic-based approaches struggle to keep up with new phishing techniques. Deep learning-based models, particularly CNNs, have proven effective in analyzing complex patterns in URLs, making them suitable for phishing detection. This project aims to develop a CNN-based model that classifies URLs as phishing or legitimate, providing a more robust and scalable solution for detecting phishing websites.

Problem Statement

Phishing websites pose a significant risk to online users, leading to identity theft, financial fraud, and data breaches. Existing phishing detection mechanisms are either blacklist-based, which fail to detect newly created phishing sites, or heuristic-based, which require manual rule updates. There is a need for an automated and intelligent phishing URL detection system that can accurately identify phishing websites without human intervention.

Existing System and Disadvantages

Existing System:

• Blacklist-based detection: Maintains a database of known phishing websites.
• Heuristic-based detection: Uses predefined rules to identify suspicious URLs.
• Machine Learning-based detection: Uses traditional ML classifiers (e.g., SVM, Decision Trees) trained on extracted URL features.

Disadvantages:

• Blacklist-based: Ineffective against new phishing sites (zero-day attacks).
• Heuristic-based: Requires frequent updates to maintain accuracy.
• Machine Learning-based: Relies heavily on handcrafted feature extraction, limiting adaptability.

Proposed System and Advantages

Proposed System:

The proposed system employs a CNN-based model to automatically extract and learn URL patterns, eliminating the need for manual feature engineering. The system uses deep learning techniques to classify URLs as phishing or legitimate with high accuracy.

Advantages:

• Automated feature extraction: CNNs learn patterns directly from URLs.
• Higher accuracy: CNNs outperform traditional machine learning models.
• Detects zero-day attacks: Can generalize well to new phishing attempts.
• Scalability: Can be deployed in real-time cybersecurity applications.

Modules

1. Data Collection and Pre-processing
   • Collect legitimate and phishing URLs from sources like PhishTank, OpenPhish, and Alexa.
   • Pre-process URLs by tokenizing and encoding textual features.
2. Feature Extraction using CNN
   • Use Convolutional Neural Networks (CNN) to automatically learn features from URL structures.
3. Model Training and Evaluation
   • Train the CNN model using labeled data.
   • Evaluate the model using metrics like accuracy, precision, recall, and F1-score.
4. Real-Time Phishing Detection
   • Deploy the trained model to classify new URLs.
   • Integrate with browser extensions or web security applications.
5. Performance Analysis and Optimization
   • Compare the CNN model with traditional ML models.
   • Fine-tune hyperparameters to optimize performance.

Algorithm:

The project leverages Convolutional Neural Networks (CNN) for phishing URL detection. The key components include:

1. Embedding Layer: Converts URL characters into vector representations.
2. Convolutional Layers: Extract spatial features from URLs.
3. Pooling Layers: Reduce dimensionality while retaining essential patterns.
4. Fully Connected Layer: Classifies the URL as phishing or legitimate.
5. Activation Functions: Uses ReLU in hidden layers and softmax/sigmoid for final classification.

Software and Hardware Requirements

Software Requirements:

• Programming Language: Python
• Deep Learning Framework: TensorFlow / Keras / PyTorch
• Libraries: NumPy, Pandas, Scikit-learn, Matplotlib
• Development Environment: Jupyter Notebook, Google Colab

Hardware Requirements:

• Processor: Intel Core i5/i7 or AMD equivalent
• RAM: Minimum 8GB (16GB recommended)
• Storage: At least 20GB free space

 Conclusion

Phishing attacks are a growing threat to online security, and traditional detection methods are becoming less effective. This project presents a CNN-based phishing URL detection system that improves detection accuracy by learning URL patterns automatically. By leveraging deep learning techniques, the system provides an efficient and scalable solution for identifying phishing websites in real-time.

Future Enhancements

• Integration with Web Browsers: Develop browser extensions for real-time detection.
• Hybrid Model: Combine CNN with LSTMs or transformers for improved accuracy.
• Threat Intelligence Integration: Use external APIs for enhanced phishing detection.
• Deployment on Cloud Services: Provide scalable phishing detection as a cloud-based service.