Abstract
Abstract: The growing internet user base and reliance on online platforms have led to a growing concern of phishing attacks. Conventional anti-phishing techniques struggle to keep up with evolving tactics. This research proposes a novel approach using machine learning algorithms to combat phishing attacks in real-time. The dataset includes legitimate and phishing websites, with various attack vectors and strategies. Data preprocessing, feature engineering, and machine learning models are trained on the dataset. The proposed approach achieves high accuracy and outperforms traditional rule-based methods. The ensemble models exhibit superior performance in handling both known and unseen phishing attacks. The real-time nature of the system allows for swift adaptation to new and emerging phishing techniques. The system's low computational overhead ensures seamless operation on various platforms without performance degradation.
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