Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information
Abbasi, Ahmed1,2; Zahedi, Fatemeh Mariam3; Zeng, Daniel4,5; Chen, Yan6; Chen, Hsinchun7,8; Nunamaker, Jay F., Jr.9,10,11,12,13
2015-03-01
发表期刊JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
卷号31期号:4页码:109-157
文章类型Article
摘要Phishing websites continue to successfully exploit user vulnerabilities in household and enterprise settings. Existing anti-phishing tools lack the accuracy and generalizability needed to protect Internet users and organizations from the myriad of attacks encountered daily. Consequently, users often disregard these tools' warnings. In this study, using a design science approach, we propose a novel method for detecting phishing websites. By adopting a genre theoretic perspective, the proposed genre tree kernel method utilizes fraud cues that are associated with differences in purpose between legitimate and phishing websites, manifested through genre composition and design structure, resulting in enhanced anti-phishing capabilities. To evaluate the genre tree kernel method, a series of experiments were conducted on a testbed encompassing thousands of legitimate and phishing websites. The results revealed that the proposed method provided significantly better detection capabilities than state-of-the-art anti-phishing methods. An additional experiment demonstrated the effectiveness of the genre tree kernel technique in user settings; users utilizing the method were able to better identify and avoid phishing websites, and were consequently less likely to transact with them. Given the extensive monetary and social ramifications associated with phishing, the results have important implications for future anti-phishing strategies. More broadly, the results underscore the importance of considering intention/purpose as a critical dimension for automated credibility assessment: focusing not only on the "what" but rather on operationalizing the "why" into salient detection cues.
关键词Design Science Data Mining Phishing Websites Genre Theory Internet Fraud Website Genres Credibility Assessment Phishing
WOS标题词Science & Technology ; Social Sciences ; Technology
关键词[WOS]AIDED CREDIBILITY ASSESSMENT ; VISUAL SIMILARITY ASSESSMENT ; DETECTING FAKE WEBSITES ; INTERNET FRAUD ; WEB PAGES ; CLASSIFICATION ; ATTACKS ; DESIGN ; TECHNOLOGY ; KNOWLEDGE
收录类别SCI ; SSCi
语种英语
WOS研究方向Computer Science ; Information Science & Library Science ; Business & Economics
WOS类目Computer Science, Information Systems ; Information Science & Library Science ; Management
WOS记录号WOS:000353145800006
引用统计
被引频次:15[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8115
专题复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
作者单位1.Univ Virginia, IT, Charlottesville, VA 22903 USA
2.Univ Virginia, McIntire Sch Commerce, Ctr Business Analyt, Charlottesville, VA 22903 USA
3.Univ Wisconsin Milwaukee, Sheldon B Lubar Sch Business, Informat Technol Management Area, Milwaukee, WI USA
4.Chinese Acad Sci, Inst Automat, Beijing 100864, Peoples R China
5.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
6.Auburn Univ, Montgomery, AL 36117 USA
7.Univ Arizona, Tucson, AZ 85721 USA
8.IEEE, New York, NY USA
9.Univ Arizona, MIS Comp Sci & Commun, Tucson, AZ 85721 USA
10.Univ Arizona, Ctr Management Informat, Tucson, AZ 85721 USA
11.Univ Arizona, Natl Ctr Border Secur & Immigrat, Tucson, AZ 85721 USA
12.Purdue Univ, Comp Sci, W Lafayette, IN 47907 USA
13.Univ Arizona, MIS Dept, Tucson, AZ 85721 USA
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Abbasi, Ahmed,Zahedi, Fatemeh Mariam,Zeng, Daniel,et al. Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information[J]. JOURNAL OF MANAGEMENT INFORMATION SYSTEMS,2015,31(4):109-157.
APA Abbasi, Ahmed,Zahedi, Fatemeh Mariam,Zeng, Daniel,Chen, Yan,Chen, Hsinchun,&Nunamaker, Jay F., Jr..(2015).Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information.JOURNAL OF MANAGEMENT INFORMATION SYSTEMS,31(4),109-157.
MLA Abbasi, Ahmed,et al."Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information".JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 31.4(2015):109-157.
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