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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 [20 citations]

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

  • M. Amaad Ul Haq Tahir, Sohail Asghar, Ayesha Zafar, and Saira Gillani, “A Hybrid Model to Detect Phishing-Sites Using Supervised Learning Algorithms,” 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1126–1133, . View at Publisher · View at Google Scholar
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  • Neda Abdelhamid, Fadi Thabtah, and Hussein Abdel-jaber, “Phishing detection: A recent intelligent machine learning comparison based on models content and features,” 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 72–77, . View at Publisher · View at Google Scholar
  • 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
  • Masoumeh Zareapoor, Pourya Shamsolmoali, Afshar Alam, Masoumeh Zareapoor, Pourya Shamsolmoali, and M. Afshar Alam, “Highly discriminative features for phishing email classification by SVD,” Advances in Intelligent Systems and Computing, vol. 339, pp. 649–656, 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
  • H. S. Hota, Akhilesh Kumar Shrivas, and Rahul Hota, “A Proposed Bucket Based Feature Selection Technique (BBFST) for Phishing e-Mail Classification,” Progress in Intelligent Computing Techniques: Theory, Practice, and Applications, vol. 519, pp. 189–194, 2017. View at Publisher · View at Google Scholar
  • Anthony Trotta, and Missing-Value Missing-Value, “E-Governance, Deliberative Democracy and Voting Processes,” Advances in E-Governance, pp. 158–184, 2017. 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
  • Aviad Cohen, Nir Nissim, and Yuval Elovici, “Novel Set of General Descriptive Features For Enhanced Detection of Malicious Emails Using Machine Learning Methods,” Expert Systems with Applications, 2018. 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
  • Andronicus Ayobami Akinyelu, and Aderemi Oluyinka Adewumi, “On the performance of Cuckoo search and bat algorithms based instance selection techniques for SVM speed optimization with application to e-fraud detection,” KSII Transactions on Internet and Information Systems, vol. 12, no. 3, pp. 1348–1375, 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
  • 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
  • H.S. Hota, A.K. Shrivas, and Rahul Hota, “An Ensemble Model for Detecting Phishing Attack with Proposed Remove-Replace Feature Selection Technique,” Procedia Computer Science, vol. 132, pp. 900–907, 2018. View at Publisher · View at Google Scholar