DETECTING FAKE WEBSITES: THE CONTRIBUTION OF STATISTICAL LEARNING THEORY

• 成果类型: Article
• 署名作者: Abbasi, Ahmed; Zhang, Zhu; Zimbra, David; Chen, Hsinchun; Nunamaker, Jay F., Jr.
• 署名单位: University of Wisconsin System; University of Wisconsin Milwaukee; University of Arizona
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• 发表日期: 2010
• 页码: 435-461
• 关键词: support vector machine; design theory; E-commerce; CLASSIFICATION; kernel; trust; systems; identification; FRAMEWORK; science

摘要

Fake websites have become increasingly pervasive, generating billions of dollars in fraudulent revenue at the expense of unsuspecting Internet users. The design and appearance of these websites makes it difficult for users to manually identify them as fake. Automated detection systems have emerged as a mechanism for combating fake websites, however most are fairly simplistic in terms of their fraud cues and detection methods employed Consequently, existing systems are susceptible to the myriad of obfuscation tactics used by fraudsters, resulting in highly ineffective fake website detection performance. In light of these deficiencies, we propose the development of a new class of fake website detection systems that are based on statistical learning theory (SLT). Using a design science approach, a prototype system was developed to demonstrate the potential utility of this class of systems. We conducted a series of experiments, comparing the proposed system against several existing fake website detection systems on a test bed encompassing 900 websites. The results indicate that systems grounded in SLT can more accurately detect various categories of fake websites by utilizing richer sets of fraud cues in combination with problem-specific knowledge. Given the hefty cost exacted by fake websites, the results have important implications for e-commerce and online security.