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

Xi Chen; Indranil Bose; Alvin Chung Man Leung; Chenhui Guo

doi:10.1016/j.dss.2010.08.020

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

Xi Chen
, Indranil Bose
, Alvin Chung Man Leung
, Chenhui Guo

Supply Chain and Information Systems

Research output: Contribution to journal › Article › peer-review

76 Scopus citations

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.

Original language	English (US)
Pages (from-to)	662-672
Number of pages	11
Journal	Decision Support Systems
Volume	50
Issue number	4
DOIs	https://doi.org/10.1016/j.dss.2010.08.020
State	Published - Mar 2011

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

All Science Journal Classification (ASJC) codes

Management Information Systems
Information Systems
Developmental and Educational Psychology
Arts and Humanities (miscellaneous)
Information Systems and Management

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10.1016/j.dss.2010.08.020

Cite this

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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.",

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AU - Bose, Indranil

AU - Leung, Alvin Chung Man

AU - Guo, Chenhui

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AB - 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.

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IS - 4

ER -

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

Abstract

UN SDGs

All Science Journal Classification (ASJC) codes

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