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Phishing attacks, a pernicious form of cybercrime, continue to exploit users' vulnerability to trick them into divulging sensitive information, such as usernames and passwords.This project addresses the imperative need for an enhanced phishing website detection system that outperforms its predecessors.To counter this pervasive threat, we employ the power of deep learning algorithms to scrutinize website characteristics, pinpointing suspicious patterns indicative of phishing.Central to this endeavor is the system's capacity to continually enhance its detection capabilities by accumulating data from ongoing phishing attacks.We meticulously curate a vast dataset containing website features and corresponding labels, ensuring its quality and diversity.Subsequently, we explore a range of machine learning models, meticulously train and test them, and employ various evaluation metrics to assess their performance comprehensively.
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Maddineni et al. (Sat,) studied this question.
www.synapsesocial.com/papers/68e7031db6db64358767cc13 — DOI: https://doi.org/10.56726/irjmets51292
Karthik Maddineni
Parre Sai Krishna
M. Sethu Ram
International Research Journal of Modernization in Engineering Technology and Science
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