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Article: Assessing the severity of phishing attacks: A hybrid data mining approach

TitleAssessing the severity of phishing attacks: A hybrid data mining approach
Authors
KeywordsFinancial loss
Phishing
Risk
Supervised classification
Text phrase extraction
Variable importance
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/dss
Citation
Decision Support Systems, 2011, v. 50 n. 4, p. 662-672 How to Cite?
AbstractPhishing is an online crime that increasingly plagues firms and their consumers. We assess the severity of phishing attacks in terms of their risk levels and the potential loss in market value suffered by the targeted firms. We analyze 1030 phishing alerts released on a public database as well as financial data related to the targeted firms using a hybrid method that predicts the severity of the attack with up to 89% accuracy using text phrase extraction and supervised classification. Our research identifies some important textual and financial variables that impact the severity of the attacks and potential financial loss. © 2010 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/139825
ISSN
2015 Impact Factor: 2.604
2015 SCImago Journal Rankings: 2.262
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Xen_HK
dc.contributor.authorBose, Ien_HK
dc.contributor.authorLeung, ACMen_HK
dc.contributor.authorGuo, Cen_HK
dc.date.accessioned2011-09-23T05:57:06Z-
dc.date.available2011-09-23T05:57:06Z-
dc.date.issued2011en_HK
dc.identifier.citationDecision Support Systems, 2011, v. 50 n. 4, p. 662-672en_HK
dc.identifier.issn0167-9236en_HK
dc.identifier.urihttp://hdl.handle.net/10722/139825-
dc.description.abstractPhishing is an online crime that increasingly plagues firms and their consumers. We assess the severity of phishing attacks in terms of their risk levels and the potential loss in market value suffered by the targeted firms. We analyze 1030 phishing alerts released on a public database as well as financial data related to the targeted firms using a hybrid method that predicts the severity of the attack with up to 89% accuracy using text phrase extraction and supervised classification. Our research identifies some important textual and financial variables that impact the severity of the attacks and potential financial loss. © 2010 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/dssen_HK
dc.relation.ispartofDecision Support Systemsen_HK
dc.subjectFinancial lossen_HK
dc.subjectPhishingen_HK
dc.subjectRisken_HK
dc.subjectSupervised classificationen_HK
dc.subjectText phrase extractionen_HK
dc.subjectVariable importanceen_HK
dc.titleAssessing the severity of phishing attacks: A hybrid data mining approachen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9236&volume=50&issue=4&spage=662&epage=672&date=2011&atitle=Assessing+the+severity+of+phishing+attacks:+a+hybrid+data+mining+approach-
dc.identifier.emailBose, I: bose@business.hku.hken_HK
dc.identifier.authorityBose, I=rp01041en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.dss.2010.08.020en_HK
dc.identifier.scopuseid_2-s2.0-79151482463en_HK
dc.identifier.hkuros193212en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79151482463&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume50en_HK
dc.identifier.issue4en_HK
dc.identifier.spage662en_HK
dc.identifier.epage672en_HK
dc.identifier.isiWOS:000287436700003-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridChen, X=14029590100en_HK
dc.identifier.scopusauthoridBose, I=7003751502en_HK
dc.identifier.scopusauthoridLeung, ACM=23975896100en_HK
dc.identifier.scopusauthoridGuo, C=36462303300en_HK
dc.identifier.citeulike7858546-

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