Design and development of phishing sites detector using machine learning

Selvaraju, Rugenraj (2024) Design and development of phishing sites detector using machine learning. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

This research project focuses on developing a phishing detection model to address the increasing threat of phishing attacks. The objective is to design and apply classification techniques to analyze phishing websites and improve accuracy. The study aims to provide users with effective tools to identify and protect themselves from phishing attempts, enhancing online security. The research utilizes a phishing dataset and applies techniques for model training and testing. Classification techniques are used to categorize websites as benign or phishing. The accuracy of these techniques is evaluated using metrics like confusion matrix, classification report, and accuracy score. The results demonstrate the effectiveness of the techniques in accurately detecting and classifying phishing websites. The developed model contributes to ongoing efforts in mitigating phishing attacks and protecting sensitive information by using a dual-model approach of each model having an accuracy of 74% for Logistic Regressiona and 64% for Gradient Boost Classifier respectively. In conclusion, this research shows that the classification techniques, when applied to the phishing dataset, yield promising results in identifying and classifying phishing websites. Overall, this project provides valuable insights into developing effective tools for combating phishing attacks and promoting online security.

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