Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information
文献类型：期刊论文
| 作者 | Abbasi, Ahmed1,2; Zahedi, Fatemeh Mariam3; Zeng, Daniel4,5 ; Chen, Yan6; Chen, Hsinchun7,8; Nunamaker, Jay F., Jr.9,10,11,12,13
|
| 刊名 | JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
 |
| 出版日期 | 2015-03-01 |
| 卷号 | 31期号:4页码:109-157 |
| 关键词 | design science data mining phishing websites genre theory Internet fraud website genres credibility assessment phishing |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Social Sciences ; Technology |
| 类目[WOS] | Computer Science, Information Systems ; Information Science & Library Science ; Management |
| 研究领域[WOS] | Computer Science ; Information Science & Library Science ; Business & Economics |
| 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记录号 | WOS:000353145800006 |
| 公开日期 | 2015-09-22 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/8115]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。