PhishNet: A Phishing Website Detection Tool using XGBoost
<|reference_start|>PhishNet: A Phishing Website Detection Tool using XGBoost: PhisNet is a cutting-edge web application designed to detect phishing websites using advanced machine learning. It aims to help individuals and organizations identify and prevent phishing attacks through a robust AI framework. PhisNet utilizes Python to apply various machine learning algorithms and feature extraction techniques for high accuracy and efficiency.   The project starts by collecting and preprocessing a comprehensive dataset of URLs, comprising both phishing and legitimate sites. Key features such as URL length, special characters, and domain age are extracted to effectively train the model. Multiple machine learning algorithms, including logistic regression, decision trees, and neural networks, are evaluated to determine the best performance in phishing detection. The model is finely tuned to optimize metrics like accuracy, precision, recall, and the F1 score, ensuring reliable detection of both common and sophisticated phishing tactics.   PhisNet's web application is developed using React.js, which allows for client-side rendering and smooth integration with backend services, creating a responsive and user-friendly interface. Users can input URLs and receive immediate predictions with confidence scores, thanks to a robust backend infrastructure that processes data and provides real-time results. The model is deployed using Google Colab and AWS EC2 for their computational power and scalability, ensuring the application remains accessible and functional under varying loads.   In summary, PhisNet represents a significant advancement in cybersecurity, showcasing the effective use of machine learning and web development technologies to enhance user security. It empowers users to prevent phishing attacks and highlights AI's potential in transforming cybersecurity.<|reference_end|>
@article{kumar2024phishnet:,
    title={PhishNet: A Phishing Website Detection Tool using XGBoost},
    author={Prashant Kumar, Kevin Antony, Deepakmoney Banga and Arshpreet Sohal},
    journal={arXiv preprint arXiv:2407.04732},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04732},
    primaryClass={cs.CR cs.LG}
}