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Homograph attack is a way that attackers deceive victims about which websitedomain name they are communicating with by exploiting the fact that manycharacters look alike. The attack becomes serious and is raising broadattention when recently many brand domains have been attacked such as AppleInc., Adobe Inc., Lloyds Bank, etc. We first design a survey of humandemographics, brand familiarity, and security backgrounds and apply it to 2,067participants. We build a regression model to study which factors affectparticipants' ability in recognizing homograph domains. We find that fordifferent levels of visual similarity, the participants exhibit differentabilities. 13.95% of participants can recognize non-homographs while 16.60% ofparticipants can recognize homographs whose the visual similarity with thetarget brand domains is under 99.9%; but when the similarity increases to99.9%, the number of participants who can recognize homographs significantlydrops down to only 0.19%; and for the homographs with 100% of visualsimilarity, there is no way for the participants to recognize. We also findthat female participants tend to recognize homographs better the male but maleparticipants tend to able to recognize non-homographs better than females.Security knowledge is a significant factor affecting both homographs andnon-homographs; surprisingly, people who have strong security knowledge tend tobe able to recognize homographs but not non-homographs. Furthermore, people whowork or are educated in computer science or computer engineering do not appearas a factor affecting the ability in recognizing homographs; however,interestingly, right after they are explained about the homograph attack,people who work or are educated in computer science or computer engineering arethe ones who can capture the situation the most quickly.

Influences of Human Demographics, Brand Familiarity and Security Backgrounds on Homograph Recognition