Cybersecurity Empirics: Evaluating Machine Learning Techniques for Phishing Detection
Samar Hendawi, Yaser Jararweh, Yazan Zreqat, Shadi Mahmoud Faleh AlZu’bi · 2023
TIn an age dominated by internet usage, the threat of phishing attacks continues to plague users and organizations alike. Traditional cybersecurity mechanisms often fail to cope with the evolving tactics of cybercriminals, leading to a growing interest in machine learning-based solutions. This research evaluates the performance of four different machine learning classifiers-K-Nearest Neighbors (KNN), Naïve Bayes (NB), Decision Tree, and Artificial Neural Network (ANN)-in identifying phishing activities. A Python code framework is utilized for this evaluation, employing stratified k-fold cross-validation for reliable results. Multiple performance metrics indicate that machine learning provides an adaptable and effective tool against phishing. This research serves as a stepping stone for future development in this crucial area, urging continuous advancements to stay ahead of ever-evolving cybersecurity threats.