Paper Info
Title
HumanBoost: Utilization of Users' Past Trust Decision for Identifying Fraudulent Websites
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.
Year
DOI
Venue
2010
10.1007/978-3-642-10684-2_61
Journal of Intelligent Learning Systems and Applications
Keywords
Field
DocType
pilot study,binary vector,new heuristic,legitimate site,past trust decision,identifying fraudulent websites,detection accuracy,trust decision,phishing site,existing heuristics,web user,error rate,user requirements
Heuristic,World Wide Web,AdaBoost,Phishing,Computer science,Word error rate,Heuristics,Personally identifiable information
Journal
Volume
Issue
Citations 
2
4
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Daisuke Miyamoto100.34
Hiroaki Hazeyama216516.75
Youki Kadobayashi346365.10