Securing Smartphone from Mobile Phishing Attacks Using GoogLeNet Model
Brij Bhooshan Gupta, Akshat Gaurav, Kwok Tai Chui · 2024
Nowadays, smartphones have personal and private information about the user; hence, attackers target smartphones to access personal and confidential information. In this context, this paper proposed a googLeNet-based mobile phishing attack detection model. In our propsed model, whenever a user visits a webpage, its screenshot is analyzed by the googLeNet model, and if the website is malicious, the model alerts the user. We used GoogLeNet because it is trained on large amounts of deserts and works efficiently to detect multiclass images. Our model achieves an accuracy of 97.04%, which presents the effectiveness of our proposed model. We also compared the performance of our model with the traditional machine learning model.