Open Access
ITM Web Conf.
Volume 46, 2022
International Conference on Engineering and Applied Sciences (ICEAS’22)
Article Number 02003
Number of page(s) 4
Section Computer Sciences
Published online 06 June 2022
  1. M. Belouch, S. El Hadaj, and M. Idhammad. A two-stage classifier approach using reptree algorithm for network intrusion detection. International Journal of Advanced Computer Science and Applications, 8(6), pp.389-394 (2017) [CrossRef] [Google Scholar]
  2. M. Belouch, S. El Hadaj, & M. Idhammad. Performance evaluation of intrusion detection based on machine learning using Apache Spark. Procedia Computer Science, 127, 1-6,(2018). [CrossRef] [Google Scholar]
  3. N. Moustafa, N. (2017). Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic (Doctoral dissertation, University of New South Wales, Canberra, Australia). (2017) [Google Scholar]
  4. W. Richert, L. P. Coelho, “Building Machine Learning Systems with Python”, Packt Publishing Ltd., ISBN 978-1-78216-140-0 [Google Scholar]
  5. M. Bkassiny, Y. Li, and S. K. Jayaweera, “A survey on machine learning techniques in cognitive radios,” IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1136–1159, 2012. [Google Scholar]
  6. A. Iftikhar, M. Basheri, M. Javed Iqbal, A. Raheem; “Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection”, IEEE ACCESS, Survivability Strategies for Emerging Wireless Networks, 6,pp.33789-33795, (2018). [Google Scholar]

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