Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information
	Abbasi, Ahmed 1,2; Zahedi, Fatemeh Mariam 3; Zeng, Daniel4,5 ; Chen, Yan 6; Chen, Hsinchun 7,8; Nunamaker, Jay F., Jr.9,10,11,12,13
发表期刊	JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
	2015-03-01
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：52[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	多模态人工智能系统全国重点实验室_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
推荐引用方式 GB/T 7714	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.