PhishTime: Continuous Longitudinal Measurement of the Effectiveness of Anti-phishing Blacklists

Due to their ubiquity in modern web browsers, antiphishing blacklists are a key defense against large-scale phishing attacks. However, sophistication in phishing websites-such as evasion techniques that seek to defeat these blacklists-continues to grow. Yet, the e ectiveness of blacklists against evasive websites is di cult to measure, and there have been no methodical e orts to make and track such measurements, at the ecosystem level, over time. We propose a framework for continuously identifying unmitigated phishing websites in the wild, replicating key aspects of theircon guration in a controlled setting,and generating longitudinal experiments to measure the ecosystem's protection. In six experiment deployments over nine months, we systematically launch and report 2,862 new (innocuous) phishing websites to evaluate the performance (speed and coverage) and consistency of blacklists, with the goal of improving them. We show that methodical long-term empirical measurements are an e ective strategy for proactively detecting weaknesses in the anti-phishing ecosystem. Through our experiments, we identify and disclose several such weaknesses, including a class of behavior-based JavaScript evasion that blacklists were unable to detect. We nd that enhanced protections on mobile devices and the expansion of evidence-based reporting protocols are critical ecosystem improvements that could better protect users against modern phishing attacks, which routinely seek to evade detection infrastructure.