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Journal of Applied Mathematics
Volume 2014, Article ID 425731, 6 pages
http://dx.doi.org/10.1155/2014/425731
Research Article

Classification of Phishing Email Using Random Forest Machine Learning Technique

School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Private Bag Box X54001, Durban 4000, South Africa

Received 23 January 2014; Accepted 11 March 2014; Published 3 April 2014

Academic Editor: Olabisi Falowo

Copyright © 2014 Andronicus A. Akinyelu and Aderemi O. Adewumi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [11 citations]

The following is the list of published articles that have cited the current article.

  • Taejin Lee, Hesun Cho, Haeryong Park, and Jin Kwak, “Detection of Malware Propagation in Sensor Node and Botnet Group Clustering Based on E-mail Spam Analysis,” International Journal of Distributed Sensor Networks, vol. 2015, pp. 1–12, 2015. View at Publisher · View at Google Scholar
  • M. U. Chowdhury, J. H. Abawajy, A. V. Kelarev, and T. Hochin, “Multilayer hybrid strategy for phishing email zero-day filtering,” Concurrency and Computation: Practice and Experience, 2016. View at Publisher · View at Google Scholar
  • Oluyinka Aderemi Adewumi, and Ayobami Andronicus Akinyelu, “A hybrid firefly and support vector machine classifier for phishing email detection,” Kybernetes, vol. 45, no. 6, pp. 977–994, 2016. View at Publisher · View at Google Scholar
  • Bandar Alotaibi, and Khaled Elleithy, “A New MAC Address Spoofing Detection Technique Based on Random Forests,” Sensors, vol. 16, no. 3, pp. 281, 2016. View at Publisher · View at Google Scholar
  • Malathi, and Vivekanandan, “Stream life estimation based on the end point elimination technique to restrict client side script for secure computing,” Asian Journal of Information Technology, vol. 15, no. 19, pp. 3846–3851, 2016. View at Publisher · View at Google Scholar
  • Yok-Yen Nguwi, and Pek Ru Loh, “Comparative Study of Feature Selection and Classification for Problematic Mobile Phone Use (PMPU),” Journal of Technology in Behavioral Science, 2017. View at Publisher · View at Google Scholar
  • Ghulam Mujtaba, Liyana Shuib, Ram Gopal Raj, Nahdia Majeed, and Mohammed Ali Al-Garadi, “Email Classification Research Trends: Review and Open Issues,” IEEE Access, vol. 5, pp. 9044–9064, 2017. View at Publisher · View at Google Scholar
  • Andronicus A. Akinyelu, and Aderemi O. Adewumi, “Improved Instance Selection Methods for Support Vector Machine Speed Optimization,” Security and Communication Networks, vol. 2017, pp. 1–11, 2017. View at Publisher · View at Google Scholar
  • Gunikhan Sonowal, and K S Kuppusamy, “SmiDCA: An Anti-Smishing Model with Machine Learning Approach,” The Computer Journal, 2018. View at Publisher · View at Google Scholar
  • Ike Vayansky, and Sathish Kumar, “Phishing – challenges and solutions,” Computer Fraud & Security, vol. 2018, no. 1, pp. 15–20, 2018. View at Publisher · View at Google Scholar
  • Routhu Srinivasa Rao, and Alwyn Roshan Pais, “Detection of phishing websites using an efficient feature-based machine learning framework,” Neural Computing and Applications, 2018. View at Publisher · View at Google Scholar