Majority Voting and Feature Selection Based Network Intrusion Detection System

Authors

  • Dharmaraj R. Patil R.C. Patel Institute of Technology
  • Tareek M. Pattewar Vishwakarma University image/svg+xml

DOI:

https://doi.org/10.4108/eai.4-4-2022.173780

Keywords:

Network Intrusion detection system, Feature selection, Majority voting, Machine learning, NSL_KDD, Network security

Abstract

Attackers continually foster new endeavours and attack strategies meant to keep away from safeguards. Many attacks have an effect on other malware or social engineering to collect consumer credentials that grant them get access to network and data. A network intrusion detection system (NIDS) is essential for network safety because it empowers to understand and react to malicious traffic. In this paper, we propose a feature selection and majority voting based solutions for detecting intrusions. A multi-model intrusion detection system is designed using Majority Voting approach. Our proposed approach was tested on a NSL-KDD benchmark dataset. The experimental results show that models based on Majority Voting and Chi-square features selection method achieved the best accuracy of 99.50% with error-rate of 0.501%, FPR of 0.005 and FNR of 0.005 using only 14 features.

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Published

04-04-2022

How to Cite

1.
R. Patil D, M. Pattewar T. Majority Voting and Feature Selection Based Network Intrusion Detection System. EAI Endorsed Scal Inf Syst [Internet]. 2022 Apr. 4 [cited 2024 Mar. 29];9(6):e6. Available from: https://publications.eai.eu/index.php/sis/article/view/350