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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)662-672
Journal / PublicationDecision Support Systems
Volume50
Issue number4
Online published19 Aug 2010
Publication statusPublished - Mar 2011
Externally publishedYes

Abstract

Phishing 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.

Research Area(s)

  • Financial loss, Phishing, Risk, Supervised classification, Text phrase extraction, Variable importance

Citation Format(s)

Assessing the severity of phishing attacks : A hybrid data mining approach. / Chen, Xi; Bose, Indranil; Leung, Alvin Chung Man; Guo, Chenhui.

In: Decision Support Systems, Vol. 50, No. 4, 03.2011, p. 662-672.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review