Advertisement

Identification of Phishing Attack in Websites Using Random Forest-SVM Hybrid Model

  • Amritanshu Pandey
  • Noor Gill
  • Kashyap Sai Prasad Nadendla
  • I. Sumaiya ThaseenEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

Phishing attacks have increased in the last few years with the rapid growth of economy and technology. Attackers with less technical knowledge can also perform phishing with sources that are available in public. Financial losses are experienced by businesses and customers thus decreasing confidence in e-commerce. Hence there is a necessity to implement countermeasures to overcome phishing attacks in the website. In this paper, a hybrid model is proposed integrating Random Forest and Support Vector Machine (SVM) techniques. Machine learning models are efficient in prediction and analyze large volumes of data. Experimental results on the phishing datasets how that an accuracy of 94% is obtained by the hybrid model in comparison to the base classifier SVM accuracy of 90% and Random Forest accuracy of 92. 96%. Thus, the model is superior in classifying the phishing attacks.

Keywords

Accuracy Feature Selection Phishing Random Forest Support Vector Machine 

References

  1. 1.
    Abdelhamid, N., Ayesh, A., Thabtah, F.: Phishing detection based associative classification data mining. Expert Syst. Appl. 41(13), 5948–5959 (2014).  https://doi.org/10.1016/j.eswa.2014.03.019CrossRefGoogle Scholar
  2. 2.
    Abu-Nimeh, S., Nappa, D., Wang, X., Nair, S.: A comparison of machine learning techniques for phishing detection. In: Proceedings of the Anti-Phishing Working Group eCrime Researchers Summit, pp. 60–69. ACM, New York (2007).  https://doi.org/10.1145/1299015.1299021
  3. 3.
    Aburrous, M., Hossain, M.A., Dahal, K.: Experimental case studies for investigating e-banking phishing techniques and attack strategies. Cogn. Comput. 2(3), 242–253 (2010).  https://doi.org/10.1007/s12559-010-9042-7CrossRefGoogle Scholar
  4. 4.
    Alkhozae, M.G., Batarfi, O.A.: Phishing websites detection based on phishing characteristics in the webpage source code. Int. J. Inf. Commun. Technol. Res. 1(9), 238–291 (2011)Google Scholar
  5. 5.
    Barraclough, P., Hossain, M., Tahir, M., Sexton, G., Aslam, N.: Intelligent phishing detection and protection scheme for online transactions. Expert Syst. Appl. 40(11), 4697–4706 (2013).  https://doi.org/10.1016/j.eswa.2013.02.009CrossRefGoogle Scholar
  6. 6.
    Barakat, N., Bradley, A.: Rule extraction from support vector machines: a review. Neurocomputing 74(1–3), 178–190 (2010).  https://doi.org/10.1016/j.neucom.2010.02.016CrossRefGoogle Scholar
  7. 7.
    Rami, M., Thabtah, F.A., McCluskey, T.: Intelligent rule-based phishing websites classification. Inf. Secur. IET 8(3), 153–160 (2014).  https://doi.org/10.1049/iet-ifs.2013.0202CrossRefGoogle Scholar
  8. 8.
    Huang, H., Qian, L., Wang, Y.: An SVM-based technique to detect phishing URLs. Inf. Technol. J. 11(7), 921–925 (2012).  https://doi.org/10.3923/itj.2012.921.925CrossRefGoogle Scholar
  9. 9.
    Lakshmi, V.S., Vijaya, M.S.: Efficient prediction of phishing websites using supervised learning algorithms. Procedia Eng. 30, 798–805 (2012).  https://doi.org/10.1016/j.proeng.2012.01.930CrossRefGoogle Scholar
  10. 10.
    Moghimi, M., Varjani, A.Y.: New rule-based phishing detection method. Expert Syst. Appl. 53, 231–242 (2016)CrossRefGoogle Scholar
  11. 11.
    Yue, X., Abraham, A., Chi, Z.X., Hao, Y.Y., Mo, H.W.: Artificial immune system inspired behavior based anti-spam filter. Soft Comput.: Soft Comput. - Fusion Found. Methodol. Appl. 11(8), 729–740 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Amritanshu Pandey
    • 1
  • Noor Gill
    • 1
  • Kashyap Sai Prasad Nadendla
    • 1
  • I. Sumaiya Thaseen
    • 1
    Email author
  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia

Personalised recommendations