An Anti-Phishing System Employing Diffused Information

Abstract

The phishing scam and its variants are estimated to cost victims billions of dollars per year. Researchers have responded with a number of anti-phishing systems, based either on blacklists or on heuristics. The former cannot cope with the churn of phishing sites, while the latter usually employ decision rules that are not congruent to human perception. We propose a novel heuristic anti-phishing system that explicitly employs gestalt and decision theory concepts to model perceptual similarity. Our system is evaluated on three corpora contrasting legitimate Web sites with real-world phishing scams. The proposed system’s performance was equal or superior to current best-of-breed systems. We further analyze current anti-phishing warnings from the perspective of warning theory, and propose a new warning design employing our Gestalt approach.