Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining

Maher Aburrous, M. A. Hossain, Keshav Dahal, Fadi Thabatah

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Detecting and identifying any phishing websites in real-time, particularly for e-banking is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the 'fuzziness' in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria's with a layer structure. The proposed e-banking phishing website model showed the significance importance of the phishing website two criteria's (URL & Domain Identity) and (Security & Encryption) in the final phishing detection rate result, taking into consideration its characteristic association and relationship with each others as showed from the fuzzy data mining classification and association rule algorithms. Our phishing model also showed the insignificant trivial influence of the (Page Style & Content) criteria along with (Social Human Factor) criteria in the phishing detection final rate result.
Original languageEnglish
Title of host publicationCyberWorlds, 2009. CW '09. International Conference on
PublisherIEEE
Pages265-272
ISBN (Print)978-1-4244-4864-7
DOIs
Publication statusPublished - 2009
Externally publishedYes

Keywords

  • Phishing
  • Fuzzy Logic
  • data mining
  • classification
  • association
  • apriori
  • e-banking risk assessment

Cite this

Aburrous, M., Hossain, M. A., Dahal, K., & Thabatah, F. (2009). Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining. In CyberWorlds, 2009. CW '09. International Conference on (pp. 265-272). IEEE. https://doi.org/10.1109/CW.2009.43
Aburrous, Maher ; Hossain, M. A. ; Dahal, Keshav ; Thabatah, Fadi. / Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining. CyberWorlds, 2009. CW '09. International Conference on. IEEE, 2009. pp. 265-272
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abstract = "Detecting and identifying any phishing websites in real-time, particularly for e-banking is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the 'fuzziness' in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria's with a layer structure. The proposed e-banking phishing website model showed the significance importance of the phishing website two criteria's (URL & Domain Identity) and (Security & Encryption) in the final phishing detection rate result, taking into consideration its characteristic association and relationship with each others as showed from the fuzzy data mining classification and association rule algorithms. Our phishing model also showed the insignificant trivial influence of the (Page Style & Content) criteria along with (Social Human Factor) criteria in the phishing detection final rate result.",
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Aburrous, M, Hossain, MA, Dahal, K & Thabatah, F 2009, Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining. in CyberWorlds, 2009. CW '09. International Conference on. IEEE, pp. 265-272. https://doi.org/10.1109/CW.2009.43

Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining. / Aburrous, Maher; Hossain, M. A.; Dahal, Keshav; Thabatah, Fadi.

CyberWorlds, 2009. CW '09. International Conference on. IEEE, 2009. p. 265-272.

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining

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AU - Dahal, Keshav

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PY - 2009

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N2 - Detecting and identifying any phishing websites in real-time, particularly for e-banking is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the 'fuzziness' in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria's with a layer structure. The proposed e-banking phishing website model showed the significance importance of the phishing website two criteria's (URL & Domain Identity) and (Security & Encryption) in the final phishing detection rate result, taking into consideration its characteristic association and relationship with each others as showed from the fuzzy data mining classification and association rule algorithms. Our phishing model also showed the insignificant trivial influence of the (Page Style & Content) criteria along with (Social Human Factor) criteria in the phishing detection final rate result.

AB - Detecting and identifying any phishing websites in real-time, particularly for e-banking is really a complex and dynamic problem involving many factors and criteria. Because of the subjective considerations and the ambiguities involved in the detection, Fuzzy Data Mining (DM) Techniques can be an effective tool in assessing and identifying phishing websites for e-banking since it offers a more natural way of dealing with quality factors rather than exact values. In this paper, we present novel approach to overcome the 'fuzziness' in the e-banking phishing website assessment and propose an intelligent resilient and effective model for detecting e-banking phishing websites. The proposed model is based on Fuzzy logic (FL) combined with Data Mining algorithms to characterize the e-banking phishing website factors and to investigate its techniques by classifying there phishing types and defining six e-banking phishing website attack criteria's with a layer structure. The proposed e-banking phishing website model showed the significance importance of the phishing website two criteria's (URL & Domain Identity) and (Security & Encryption) in the final phishing detection rate result, taking into consideration its characteristic association and relationship with each others as showed from the fuzzy data mining classification and association rule algorithms. Our phishing model also showed the insignificant trivial influence of the (Page Style & Content) criteria along with (Social Human Factor) criteria in the phishing detection final rate result.

KW - Phishing

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KW - e-banking risk assessment

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DO - 10.1109/CW.2009.43

M3 - Chapter

SN - 978-1-4244-4864-7

SP - 265

EP - 272

BT - CyberWorlds, 2009. CW '09. International Conference on

PB - IEEE

ER -

Aburrous M, Hossain MA, Dahal K, Thabatah F. Modelling Intelligent Phishing Detection System for e-Banking using Fuzzy Data Mining. In CyberWorlds, 2009. CW '09. International Conference on. IEEE. 2009. p. 265-272 https://doi.org/10.1109/CW.2009.43