Title:
Detecting Phishing URLs With CNN - SVM Method

Authors:
Fetty Tri Anggraeny; Reza Aminullah, Indonesia

Abstract:

This study aims to evaluate the effectiveness of the Convolutional Neural Network ( CNN ) method combined with Support Vector Machine ( SVM ) in detecting URLs. Phishing. Phishing is one of the significant cyber threats, where attackers try to trick users into providing sensitive information through fake websites. With the increasing number of phishing attacks , there is a need for effective methods to detect and prevent this threat. In this study, a dataset containing URLs phishing and non- phishing data were used to train the CNN - SVM model . The training process involved feature extraction from URLs using CNN , which is capable of capturing complex patterns in the data, followed by classification using SVM , which is known for its ability to handle high-dimensional data. Testing was conducted across nine different scenarios to evaluate the performance of the model under various conditions. The test results showed that the hybrid CNN - SVM model achieved a precision of 95%, a recall of 92%, and an F1-Score of 93%, with an overall accuracy of 94%. These results indicate that the model is not only effective in detecting URLs phishing , but also has a good balance between precision and recall. This study indicates that the combination of CNN and SVM can be an effective solution for detecting URLs phishing , making a significant contribution to the development of better cyber security systems.

DOI: http://dx.doi.org/10.51505/ijaemr.2025.1310