Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website

Abstract

Web browsing has become an integral part of our daily lives, with most modern computer devices supporting easy access to online services and information. However, this convenience comes with a significant risk to user security. Web users are exposed to various types of cyberattacks, such as Phishing, malware, profiling, etc. These hazards have the potential to compromise individuals or organizations and deny lists. The traditional Phishing defense is no longer effective in shielding users from the constantly evolving nature of Phishing Uniform Resource Locators (URLs). To address this issue, this work proposes a One-Dimensional Convolutional Neural Networks (1D-CNN) and Feed-Forward Convolutional Neural Network (FF-CNN)-based Phishing URL detection approach. The proposed approach is trained with three different datasets: a URL-based feature dataset, an embedded feature-based dataset, and a combination of both feature datasets. Experiments show that the proposed 1D-CNN-based approach achieved detection accuracy of 98.83%, 98.09%, and 98.91% on the URL-based features dataset, embedded features dataset, and combined features dataset, respectively. Furthermore, the proposed FF-CNN-based approach achieved an accuracy of 98.87%, 97.18%, and 98.78% on the same datasets. This research provides an effective approach to combating the growing threat of web-based attacks and safeguarding the security of web users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.