Enhancing Phishing Detection: A Machine Learning Approach With Feature Selection and Deep Learning Models

With the rise in cybercrime, phishing remains a significant concern as it targets individuals with fake websites, causing victims to disclose their private information. The effective implementation of phishing detection relies on cost efficiency, with the increased feature extraction factor contributing to these costs. This research analyzes a dataset containing 58,645 URLs, examining 111 features of the latest phishing websites dataset to identify the differences between phishing sites and legitimate sites. Astonishingly, using only 14 characteristics, the feedforward model achieved a remarkable accuracy of 94.46%, confirming the efficiency of Machine Learning in phishing detection. Through the exploitation of a multiple classifier collection, including Deep Neural Network (DNN), Wide and Deep, and TabNet, this research advances ongoing efforts to improve the accuracy and efficiency of phishing detection mechanisms and enhance cybersecurity defenses against malicious activities. The methodology introduces a new metric called the 'anti-phishing score,' which evaluates performance based on false positives and negatives, beyond traditional model accuracy. The model was trained through a robust design of extensive experimentation and hyperparameter-sensitive grid search, ensuring an optimized configuration for phishing detection. Furthermore, the trained model was validated on a new dataset to evaluate its generalizability, enhancing its practical applicability. Through the integration of feature selection principles, advanced algorithmic techniques, and comprehensive evaluation approaches, this research offers a robust approach to phishing detection, considering the evolving nature of cyber threats. The findings provide a beneficial framework for cybersecurity specialists and researchers, enabling more effective preventive measures against phishing attacks.