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Automated Detection of Phishing Websites Using Machine Learning

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    Project Mart
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Introduction

Phishing attacks pose significant threats to online security, often leading to data breaches and financial losses. The automated detection of phishing websites is crucial for enhancing cybersecurity measures. This project proposal focuses on developing a machine learning-based system to identify phishing websites effectively, drawing inspiration from contemporary research in the field.

Background

Recent advancements in machine learning have demonstrated its potential in detecting phishing websites by analyzing various features such as URL characteristics, webpage content, and metadata. Techniques like decision trees, random forests, and neural networks have been employed to improve detection accuracy and reduce false positives.

Project Objective

The primary objective of this project is to create a robust system capable of automatically detecting phishing websites with high accuracy. The system will utilize machine learning algorithms to analyze and classify websites based on their likelihood of being phishing sites.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Use publicly available datasets such as PhishTank and UCI Machine Learning Repository for training and evaluation.
  • Feature Extraction: Extract features including URL length, presence of special characters, domain age, and HTTPS usage.

2. Model Architecture

  • Algorithm Selection: Implement algorithms such as Random Forests, Support Vector Machines (SVM), and Neural Networks for classification.
  • Feature Engineering: Enhance feature sets by incorporating additional metadata and content-based features.

3. Training and Evaluation

  • Training: Train models using labeled datasets with a focus on minimizing false positives.
  • Evaluation Metrics: Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC.

Expected Outcomes

The proposed system is expected to achieve high accuracy in detecting phishing websites while maintaining low false positive rates. By leveraging machine learning techniques, the system should efficiently handle new and evolving phishing tactics.

Conclusion

This project aims to advance the field of cybersecurity by developing an automated system for phishing website detection. The integration of various machine learning algorithms is anticipated to provide significant improvements in identifying phishing threats accurately.

For further details on related research, please refer to the paper "Automated Detection of Phishing Websites Using Machine Learning," available at [https://www.sciencedirect.com/science/article/pii/S1877050920316318].

Dataset link: PhishTank

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