%0 Journal Article %1 noauthororeditor %A Al-Ahmadi, Saad %A Alharbi, Yasser %D 2020 %J International Journal of Computer Networks & Communications (IJCNC) %K Phishing URL convolution deep detection learning network neural similarity visual %N 5 %P 41-54 %R 10.5121/ijcnc.2020.12503 %T A Deep Learning Technique for Web Phishing Detection Combined URL Features and Visual Similarity %U https://aircconline.com/ijcnc/V12N5/12520cnc03.pdf %V 12 %X The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.

@article{noauthororeditor, abstract = {The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack. }, added-at = {2020-10-21T07:36:37.000+0200}, author = {Al-Ahmadi, Saad and Alharbi, Yasser}, biburl = {https://www.bibsonomy.org/bibtex/2120f1947c021990a4c68e0a7d1ec3a14/laimbee}, doi = {10.5121/ijcnc.2020.12503}, interhash = {97825f97d4442fac2b2775b62ec36b73}, intrahash = {120f1947c021990a4c68e0a7d1ec3a14}, issn = {ISSN 0974 - 9322 (Online); 0975 - 2293 (Print)}, journal = {International Journal of Computer Networks & Communications (IJCNC)}, keywords = {Phishing URL convolution deep detection learning network neural similarity visual}, language = {english}, month = {september}, number = 5, pages = {41-54}, timestamp = {2020-10-21T07:36:37.000+0200}, title = {A Deep Learning Technique for Web Phishing Detection Combined URL Features and Visual Similarity }, url = {https://aircconline.com/ijcnc/V12N5/12520cnc03.pdf}, volume = 12, year = 2020 }