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 journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 662-672 |
| Journal / Publication | Decision Support Systems |
| Volume | 50 |
| Issue number | 4 |
| Online published | 19 Aug 2010 |
| Publication status | Published - Mar 2011 |
| Externally published | Yes |
Link(s)
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 journal › peer-review
