Abstract
In this paper, we present an approach that aims to study users' past trust decisions (PTDs) for improving the accuracy of detecting phishing sites. Generally, Web users required to make trust decisions whenever their personal information is asked for by websites. We assume that the database of users' PTDs would be transformed into a binary vector, representing phishing or not, and the binary vector can be used for detecting phishing sites similar to the existing heuristics. For our pilot study, we invited 10 participants and performed a subject experiment in November 2007. The participants browsed 14 emulated phishing sites and 6 legitimate sites, and checked whether the site appeared to be a phishing site or not. By utilizing participants' trust decision as a new heuristic, we let AdaBoost incorporate the heuristic into 8 existing heuristics. The results show that the average error rate in the case of HumanBoost is 9.5%, whereas that in the case of participants is 19.0% and that in the case of AdaBoost is 20.0%. Thus, we conclude that HumanBoost has a potential to improve the detection accuracy for each Web user.
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