sis 18: e48

Research Article

Majority Voting and Feature Selection Based Network Intrusion Detection System

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  • @ARTICLE{10.4108/eai.4-4-2022.173780,
        author={Dharmaraj R. Patil and Tareek M. Pattewar},
        title={Majority Voting and Feature Selection Based Network Intrusion Detection System},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={4},
        keywords={Network Intrusion detection system, Feature selection, Majority voting, Machine learning, NSL_KDD, Network security},
        doi={10.4108/eai.4-4-2022.173780}
    }
    
  • Dharmaraj R. Patil
    Tareek M. Pattewar
    Year: 2022
    Majority Voting and Feature Selection Based Network Intrusion Detection System
    SIS
    EAI
    DOI: 10.4108/eai.4-4-2022.173780
Dharmaraj R. Patil1,*, Tareek M. Pattewar2
  • 1: Department of Computer Engineering, R.C. Patel Institute of Technology, Shirpur, Maharashtra, India
  • 2: Department of Computer Engineering, Vishwakarma University, Pune, Maharashtra, India
*Contact email: dharmaraj.patil@rcpit.ac.in

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.