Advertisement

EKRV: Ensemble of kNN and Random Committee Using Voting for Efficient Classification of Phishing

  • A. NiranjanEmail author
  • D. K. Haripriya
  • R. Pooja
  • S. Sarah
  • P. Deepa Shenoy
  • K. R. Venugopal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 713)

Abstract

Any efficient anti-phishing tool must be able to classify phishing activity as ‘phishing’ with utmost accuracy. The key factor that influences the accuracy of an anti-phishing tool is the selection of a classification algorithm whose prediction accuracy is the maximum with nil or least false-positive rate. This paper proposes the implementation of a hybrid approach involving random committee that is a type of Ensemble classification technique and k-nearest neighbor (kNN) algorithm which is available as IBK (instance-based with k neighbors) on WEKA, resulting in most encouraging prediction accuracy values. The proposed scheme is followed after the preprocessing phase that involves feature extraction using Consistency Subset Eval algorithm with the Greedy Stepwise search technique.

Keywords

Random committee kNN Phishing Voting Ensemble classifiers 

References

  1. 1.
    Niranjan, A., Nitish, A., Deepa Shenoy, P., Venugopal, K.R.: Security in data mining—a comprehensive survey. Global J. Comput. Sci. Technol. 16(5), 52—73 (2017)Google Scholar
  2. 2.
    Lakshmi, V.S., Vijaya, M.S.: Efficient prediction of phishing websites using supervised learning algorithms. Procedia Eng. 30, 798—805 (2012)Google Scholar
  3. 3.
    Hadi, W., Aburub, F., Alhawari, S.: A new fast associative classification algorithm for detecting phishing websites. Appl. Soft Comput. 48, 729–734 (2016)CrossRefGoogle Scholar
  4. 4.
    Yan, Z.: A genetic algorithm based model for Chinese phishing E-commerce websites detection. In: International Conference on HCI in Business, Government and Organizations, pp. 270—279 (2016)Google Scholar
  5. 5.
    Chowdhury, M.U., Abawajy, J.H., Kelarev, A.V., Hochin, T.: Multilayer hybrid strategy for phishing email zero‐day filtering. In: Concurrency and Computation: Practice and Experience (2016)CrossRefGoogle Scholar
  6. 6.
    Shah, R.K., Hossin, M.A., Khan, A.: Intelligent phishing possibility detector. Int. J. Comput. Appl. 148(7), pp. 1–8 (2016)Google Scholar
  7. 7.
    Moghimi, M., Varjani, A.Y.: New rule-based phishing detection method. Expert Syst. Appl. 53, 231–242 (2016)CrossRefGoogle Scholar
  8. 8.
    Mohammad, R.M., Thabtah, F., McCluskey, L.: Phishing Websites Features (2015). http://eprints.hud.ac.uk/24330/6/RamiPhishing_Websites_Features.pdf

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • A. Niranjan
    • 1
    Email author
  • D. K. Haripriya
    • 1
  • R. Pooja
    • 1
  • S. Sarah
    • 1
  • P. Deepa Shenoy
    • 1
  • K. R. Venugopal
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of Engineering, Bangalore UniversityBengaluruIndia

Personalised recommendations