Addressing Phishing Threats Using A Metaheuristic Perspective On Machine Learning Classification Models Code

Web phishing attacks have emerged as a significant threat to online security, enabling phishers to steal sensitive financial information and commit fraud. To combat this, many anti-phishing systems have been developed, focusing on detecting phishing content in online communications. This study introduces novel approaches to enhance phishing detection by employing machine learning techniques. Specifically, three different single models were analyzed: Random Forest Classifier (RFC), Adaptive Boosting Classification (ADAC), and Na & iuml;ve Bayes Classification Algorithm (NBC). These models were optimized using Artificial Rabbits Optimization (ARO), resulting in hybrid models RFAR, NBAR, and ADAR. The results of the models' analysis indicate that the RFAR hybrid model performs better than the other single models and their optimized models. The RFAR model achieved precision scores of 0.950 for phishing websites, 0.954 for suspicious websites, and 0.872 for legitimate websites, with corresponding recall values of 0.929, 0.954, and 0.990, respectively. In comparison, the ADAR model was notably effective in classifying legitimate websites with a precision score of 0.896. The study's novelty lies in integrating ARO with traditional classifiers to create hybrid models that improve classification accuracy.