A mobile sensing method to counteract social media website impersonation

Abstract

Phishing is a serious threat to online users, especially since attackers have tremendously improved their techniques in impersonating important websites. With websites looking visually the same, users are fooled more easily. Visual similarity algorithms may help to detect and counteract some phished websites. Through similarity algorithms, the phishers play with the colors and visual properties of the website in a way that cannot be noticed by the users. However, the phishers make the unnoticed changes to fool the similarity algorithms as well. In this article, we propose an efficient phishing website detection algorithm using three-step checking. The performance results are compared to the state-of-the-art approaches that show new kinds of phishing warnings with better outcomes and less false positives. Our approach provides similar accuracy to the blacklisting methods with the advantage that it can easily classify the phishing websites with less overhead and without being victimized.