EAGER: A User-Centric Approach to the Design of Intelligent Fake Website Detection Systems

University Of Wisconsin-Milwaukee, Milwaukee WI

Investigators

Abstract

Fake websites have emerged as a major source of online fraud, accounting for billions of dollars in fraudulent revenue at the expense of unsuspecting Internet users. Existing tools for combating fake websites are not very accurate, are limited in terms of the categories and genres of fake websites they detect, and lack adequate usability?often causing users to disregard their recommendations. Hence, there remains a need for intelligent detection systems capable of accurately detecting various types and genres of fake websites and displaying recommendations in a manner that is conducive to system use. In filling this gap, this research takes a novel user-centric approach that involves an assessment of user perceptions regarding detection-system design alternatives. The research method includes an extensive theory-based controlled lab experiment, which assesses the impacts of various design alternatives (such as website categories, genres, and accuracy/time tradeoffs) on users? perceptions, behaviors, and skills (including security threat awareness, security threat appraisal, coping assessment, security behaviors, internet trust, and ability to identify fake websites). The research also develops a novel fake website detection system comprised of an intelligent hierarchical classification algorithm capable of promoting users? trust in the Internet. It utilizes a test bed of two thousand fake websites that include more than two million web pages. This work uncovers new knowledge about factors influencing individuals? online security behaviors and skills, promotes Internet trust by developing enhanced systems for identifying fake websites, and develops advanced data and web mining techniques suitable for incorporating into information systems curricula.