This study examines machine learning algorithms for phishing detection, suggesting that optimized feature selection enhances cyberattack identification.
Key Points
The study achieved an R2 value of 0.9534, indicating strong accuracy in phishing detection.
Using the whale optimization algorithm for feature selection improved the model's efficacy against phishing attacks.
Real-time fraud protection is essential due to the evolving tactics employed by phishing attackers.
The regression-based assessment method demonstrated significantly low prediction errors with RMSE of 0.1079.