Classifying Phishing Websites Using Multilayer Perceptron

Abstract

Theprevalenceofphishing asacybercrimecontinuestoescalate, posing significant threats to individuals' sensitive information. Thispaper addresses the urgent need for effective phishing detectionmethods, considering the limitations of existing approaches. The studyemploysArtificial Neural Networks, specifically Multilayer Perceptrons(MLP), trained using the backpropogation algorithm. The study also highlights MLP’s advantages in handling complex and noisy data. Through a comprehensivereview of related works, the paper identifies gaps in currentresearch and establishes the groundwork for an innovative phishing website classification framework. The proposed solution utilizes MLPs, offering a detailed explanation of themethodology, dataset, model architecture, and training processes. Theresearch concludes by summarizing key findings, emphasizing thesolution's contributions to cybersecurity, and outlining potentialavenues for future research.