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

  • Authors:
  • Xi Chen;Indranil Bose;Alvin Chung Man Leung;Chenhui Guo

  • Affiliations:
  • School of Management, Zhejiang University, China;School of Business, The University of Hong Kong, Hong Kong;School of Business, The University of Hong Kong, Hong Kong and McCombs School of Business, The University of Texas at Austin, TX, United States;Eller College of Management, The University of Arizona, AZ, United States

  • Venue:
  • Decision Support Systems
  • Year:
  • 2011

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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.