Machine Learning Approaches For Phishing Detection: A Comparative Analysis

In this study, the effectiveness of four machine learning models in detecting phishing websites is evaluated. Utilizing a diverse dataset, the analysis reveals that Random Forest emerges as the top performer, achieving a test accuracy of 91.49%. Notably, Random Forest exhibits robustness in distinguishing between legitimate websites and malicious ones. While Decision Tree, K-Nearest Neighbors, and Naive Bayes also demonstrate promise, they encounter difficulties in accurately classifying phishing URLs, especially within certain categories. The findings underscore the pivotal role of machine learning in cybersecurity defence against phishing attacks. The study suggests avenues for future research, such as enhanced feature engineering and exploration of advanced ensemble techniques and deep learning approaches for improved phishing detection. This research contributes to the ongoing endeavours to develop more resilient anti-phishing tools and bolster digital security against evolving cyber threats. Key Words: Phishing, Machine Learning, Cybersecurity, Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes, Website Detection, Feature Engineering, Ensemble Techniques, Model Comparison, Fine-Tuning.