Phishing-Alarm: Robust and Efficient Phishing Detection via Page Component Similarity

Social networks have become one of the most popular platforms for users to interact with each other. Given the huge amount of sensitive data available in social network platforms, user privacy protection on social networks has become one of the most urgent research issues. As a traditional information stealing technique, phishing attacks still work in their way to cause a lot of privacy violation incidents. In a Web-based phishing attack, an attacker sets up scam Web pages (pretending to be an important Website such as a social network portal) to lure users to input their private information, such as passwords, social security numbers, credit card numbers, and so on. In fact, the appearance of Web pages is among the most important factors in deceiving users, and thus, the similarity among Web pages is a critical metric for detecting phishing Websites. In this paper, we present a new solution, called Phishing-Alarm, to detect phishing attacks using features that are hard to evade by attackers. In particular, we present an algorithm to quantify the suspiciousness ratings of Web pages based on the similarity of visual appearance between the Web pages. Since cascading style sheet (CSS) is the technique to specify page layout across browser implementations, our approach uses CSS as the basis to accurately quantify the visual similarity of each page element. As page elements do not have the same influence to pages, we base our rating method on weighted page-component similarity. We prototyped our approach in the Google Chrome browser. Our large-scale evaluation using real-world websites shows the effectiveness of our approach. The proof of concept implementation verifies the correctness and accuracy of our approach with a relatively low performance overhead.