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URL PHISHING DETECTION SYSTEM USING MACHINE LEARNING
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ABSTRACT
Phishing websites are an increasing cybersecurity threat, which deceives users into providing sensitive information. This paper proposes a machine learning-based phishing detection system that checks URL-based attributes to ascertain the legitimacy of a website. The model is developed with a Gradient Boosting Classifier (GBC) and is trained on a dataset with 30 extracted features, which provides an accuracy rate of 97.4%. A web application based on Flask is created to enable real-time URL analysis so that users can check website safety effectively. The system provides more accurate detection and ease of use than conventional methods. Future enhancement involves integrating real-time web crawling and sophisticated deep learning methods to further enhance phishing detection.
Keywords: Phishing detection, machine learning, URL analysis, GBC,web crawling.
Edtech Publishers (OPC) Private Limited
Title: URL PHISHING DETECTION SYSTEM USING MACHINE LEARNING
Description:
ABSTRACT
Phishing websites are an increasing cybersecurity threat, which deceives users into providing sensitive information.
This paper proposes a machine learning-based phishing detection system that checks URL-based attributes to ascertain the legitimacy of a website.
The model is developed with a Gradient Boosting Classifier (GBC) and is trained on a dataset with 30 extracted features, which provides an accuracy rate of 97.
4%.
A web application based on Flask is created to enable real-time URL analysis so that users can check website safety effectively.
The system provides more accurate detection and ease of use than conventional methods.
Future enhancement involves integrating real-time web crawling and sophisticated deep learning methods to further enhance phishing detection.
Keywords: Phishing detection, machine learning, URL analysis, GBC,web crawling.
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