An ensemble classification model for improved performance of phishing detection system

Individuals and organisations are at risk of money losses and data compromise from phishing attempts. Traditional rule-based phishing detection methods fail to keep up with attacker strategies. The need for more advanced and adaptive phishing defences is growing. An ensemble classification model for phishing detection system is proposed to address this difficulty. We analysed a large dataset of known phishing assaults to identify common patterns and traits. Initially, the raw phishing data have pre-processed using quantile transformation to convert variable's distribution to normal distribution and to mitigate the impact of outliers. The proposed ensemble prediction model has segregated phishing e-mails, webpages, and other dangerous information. The classifier's performance is enhanced further by employing the random search approach to tweak a set of carefully chosen hyper-parameters. When compared to other state-of-the-art methodologies, our system's results have achieved competitive performance. Thus, the suggested model is a promising ensemble-based phishing detection solution.