TY - GEN
T1 - Securing Smartphone from Mobile Phishing Attacks Using GoogLeNet Model
AU - Gupta, Brij B.
AU - Gaurav, Akshat
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Deep Learning
KW - GoogLeNet
KW - Machine Learning Comparison
KW - Mobile Phishing Detection
KW - Smartphones
UR - https://www.scopus.com/pages/publications/85215278837
U2 - 10.1109/ISCT62336.2024.10791190
DO - 10.1109/ISCT62336.2024.10791190
M3 - Conference contribution
AN - SCOPUS:85215278837
T3 - Digest of Technical Papers - IEEE International Conference on Consumer Electronics
SP - 522
EP - 527
BT - 2024 IEEE International Symposium on Consumer Technology
T2 - 1st IEEE International Symposium on Consumer Technology, ISCT 2024
Y2 - 13 August 2024 through 16 August 2024
ER -