On the Effectiveness of Techniques to Detect Phishing Sites

Abstract

Phishing is an electronic online identity theft in which the attackers use a combination of social engineering and web site spoofing techniques to trick a user into revealing confidential information. This information is typically used to make an illegal economic profit (e.g., by online banking transactions, purchase of goods using stolen credentials, etc.). Although simple, phishing attacks are remarkably effective. As a result, the numbers of successful phishing attacks have been continuously increasing and many anti-phishing solutions have been proposed. One popular and widely-deployed solution is the integration of blacklist-based anti-phishing techniques into browsers. However, it is currently unclear how effective such blacklisting approaches are in mitigating phishing attacks in real-life. In this paper, we report our findings on analyzing the effectiveness of two popular anti-phishing solutions. Over a period of three weeks, we automatically tested the effectiveness of the blacklists maintained by Google and Microsoft with 10,000 phishing URLs. Furthermore, by analyzing a large number of phishing pages, we explored the existence of page properties that can be used to identify phishing pages.