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 has been a major problem for information systems managers and users for several years now. In 2008, it was estimated that phishing resulted in close to $50 billion in damages to U.S. consumers and businesses. Even so, research has yet to explore many of the reasons why Internet users continue to be exploited. The goal of this paper is to better understand the behavioral factors that ma...

Phishing attacks exploit users’ inability to distinguish legitimate websites from fake ones. Strategies for combating phishing include the prevention and detection of phishing scams, tools to help users identify phishing websites, and training users not to fall for phish. While a great deal of effort has been devoted to the first two approaches, less research has been done in the area of traini...

Phishing refers to the fraudulent attempt obtain sensitive information such as a user's username and password, well details about checking account or credit card, for purpose of using that malevolent purposes. It's possible phishing scams are most common form cybercrime utilised today. attacks can be launched against victims in variety contexts, including online payment industry, webmail, finan...

This article is devoted to a new iterative construction of hierarchical classifiers in SimpleCLI for the detection of phishing websites. Our new construction of hierarchical systems creates ensembles of ensembles in SimpleCLI by iteratively linking a top-level ensemble to another middle-level ensemble instead of a base classifier so that the top-level ensemble can generate a large multilevel sy...