URL PHISHING DETECTION SYSTEM USING MACHINE LEARNING
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URL PHISHING DETECTION SYSTEM USING MACHINE LEARNING
S CHIRANJEEVI,KVR SUJAL,N SAI SADWIK ,
Dr. B ABIRAMI
Dept. of CSE,
SRM IST, Chennai, India
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.