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Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset

Author

Listed:
  • Ahmed Mahfouz

    (Department of Computer Science, University of Memphis, Memphis, TN 38152, USA)

  • Abdullah Abuhussein

    (Department of Information Systems, St. Cloud State University, St. Cloud, MN 56301, USA)

  • Deepak Venugopal

    (Department of Computer Science, University of Memphis, Memphis, TN 38152, USA)

  • Sajjan Shiva

    (Department of Computer Science, University of Memphis, Memphis, TN 38152, USA)

Abstract

Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work.

Suggested Citation

  • Ahmed Mahfouz & Abdullah Abuhussein & Deepak Venugopal & Sajjan Shiva, 2020. "Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset," Future Internet, MDPI, vol. 12(11), pages 1-19, October.
  • Handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:180-:d:434713
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    Citations

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    Cited by:

    1. Diogo Teixeira & Silvestre Malta & Pedro Pinto, 2022. "A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning," Future Internet, MDPI, vol. 14(3), pages 1-17, February.
    2. Frank Cremer & Barry Sheehan & Michael Fortmann & Arash N. Kia & Martin Mullins & Finbarr Murphy & Stefan Materne, 2022. "Cyber risk and cybersecurity: a systematic review of data availability," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(3), pages 698-736, July.

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