D. Miyamoto, H. Hazeyama and Y. Kadobayashi, “An Evaluation of Machine Learning-based Methods for Detection of Phishing Sites,” Australian Journal of Intelligent Information Processing Systems, Vol. 10, No. 2, 2008, pp. 54-63.

has been cited by the following article:

• TITLE: Behind HumanBoost: Analysis of Users’ Trust Decision Patterns for Identifying Fraudulent Websites

  AUTHORS: Daisuke Miyamoto, Hiroaki Hazeyama, Youki Kadobayashi, Takeshi Takahashi

  KEYWORDS: Detection of Phishing Sites; Trust Decision; Credibility of Websites; Machine Learning; Cluster Analysis

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.4 No.4, November 28, 2012

  ABSTRACT: This paper analyzes users’ trust decision patterns for detecting phishing sites. Our previous work proposed HumanBoost [1] which improves the accuracy of detecting phishing sites by using users’ Past Trust Decisions (PTDs). Web users are generally required to make trust decisions whenever their personal information is requested by a website. Human-Boostassumed that a database of Web user’s PTD would be transformed into a binary vector, representing phishing or not-phishing, and the binary vector can be used for detecting phishing sites, similar to the existing heuristics. Here, this paper explores the types of the users whose PTDs are useful by running a subject experiment, where 309 participants- browsed 40 websites, judged whether the site appeared to be a phishing site, and described the criterion while assessing the credibility of the site. Based on the result of the experiment, this paper classifies the participants into eight groups by clustering approach and evaluates the detection accuracy for each group. It then clarifies the types of the users who can make suitable trust decisions for HumanBoost.