<![CDATA[Phishing remains a pervasive cybersecurity threat, with attackers constantly contriving new ways to deceive users and concession sensitive information. This paper explores the operation of machine learning for the analysis and detection of phishing websites. The proposed methodology involves the collection of a different dataset comprising both known phishing and licit websites. Applicable features are uprooted from these websites, encompassing aspects similar as URL structure, content analysis, and behavioral patterns. After data preprocessing and feature selection, various machine learning algorithms are employed to train models for phishing detection. The model's performance is strictly estimated using standard criteria and cross-validation ways to insure robustness and delicacy. also, hyperparameter tuning and ensemble styles are employed to optimize discovery capabilities. Real- time deployment of the trained model into web browsers or dispatch guests is essential for timely protection against phishing attacks.]]>
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