Phishing Attack Detection Through URL Analysis Using Hybrid and Traditional Machine Learning

This research aims to build a hybrid phishing URL detection model and classify the URLs as either Legitimate or Phishing. This model is compared with other baseline models such as FNN, DNN, Wide&Deep, and TabNet. Materials and Methods: Group 1 includes base models like FNN, DNN, Wide&Deep, and TabNet that were trained on different dataset and URL features. Group 2 uses a hybrid of machine learning models like SVM, RF and DT , and is trained with 11000+ URLs. Both existing models and proposed model are compared against metric values of accuracy, recall, false positive rate, and the anti-phishing score. Result: The proposed hybrid model performed well and got an accuracy of 96.06%, a recall of 96.59%, false positive rate of 0.0446 and an anti-phishing score of 0.9158. All the results were good but there is a minor variation in APS score, this is due to the composite nature of the weighted index rather than decrease in performance efficiency. Conclusion: The results show that the proposed hybrid model is more accurate and stable in detecting the phishing URLs.