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首页> 外文期刊>Journal of Intelligent Learning Systems and Applications >HumanBoost: Utilization of Users’ Past Trust Decision for Identifying Fraudulent Websites
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HumanBoost: Utilization of Users’ Past Trust Decision for Identifying Fraudulent Websites

机译：HumanBoost：利用用户过去的信任决定来识别欺诈性网站

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This paper presents HumanBoost, an approach that aims at improving the accuracy of detecting so-called phishing sites by utilizing users’ past trust decisions (PTDs). Web users are generally required to make trust decisions whenever their personal information is requested by a website. We assume that a database of user PTDs 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. For our pilot study, in November 2007, we invited 10 participants and performed a subject experiment. The participants browsed 14 simulated phishing sites and six legitimate sites, and judged whether or not the site appeared to be a phishing site. We utilize participants’ trust decisions as a new heuristic and we let AdaBoost incorporate it into eight existing heuristics. The results show that the average error rate for HumanBoost was 13.4%, whereas for participants it was 19.0% and for AdaBoost 20.0%. We also conducted two follow-up studies in March 2010 and July 2010, observed that the average error rate for HumanBoost was below the others. We therefore conclude that PTDs are available as new heuristics, and HumanBoost has the potential to improve detection accuracy for Web user.
机译：本文介绍了HumanBoost，该方法旨在通过利用用户的过去信任决策（PTD）来提高检测所谓的网络钓鱼站点的准确性。通常，每当网站请求其个人信息时，Web用户就必须做出信任决定。我们假设用户PTD的数据库将被转换为代表网络钓鱼或非网络钓鱼的二进制向量，并且该二进制向量可用于检测网络钓鱼站点，类似于现有的启发式方法。在2007年11月的试点研究中，我们邀请了10名参与者并进行了主题实验。参与者浏览了14个模拟网络钓鱼站点和6个合法站点，并判断该站点是否似乎是网络钓鱼站点。我们将参与者的信任决策作为一种新的启发式方法，让AdaBoost将其纳入现有的八种启发式方法中。结果显示，HumanBoost的平均错误率为13.4％，而参与者的平均错误率为19.0％，AdaBoost的平均错误率为20.0％。我们还在2010年3月和2010年7月进行了两项后续研究，发现HumanBoost的平均错误率低于其他错误率。因此，我们得出的结论是PTD可作为新的启发式方法使用，HumanBoost具有提高Web用户检测准确性的潜力。

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