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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Poornima Vivekanandhan
Technology and Advanced Studies
Computer Science & IT Engineering

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Tripartite optimized framework for network based intrusion detection system Poonkody Iruthayaraj; Poornima Vivekanandhan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4224-4233

Abstract

An unsecured network pays the way for various illegal access. Therefore, an efficient security system is needed to safeguard the data from attackers. Even though various network security methods such as authentication, authorization, and firewall have been used, the system still suffers from various types of security issues. An optimized soft voting classifier (OSOVOC) algorithm is proposed for network-based intrusion detection system (NIDS). It performs the hybridization of two algorithms namely: optimized decision tree (ODT) and optimized support vector machine (OSVM). All three algorithms are optimized using hyper parameter tuning. The final proposed model produces a more accurate result in an unbalanced dataset The primary focus of the proposed OSOVOC algorithm is to eradicate the false alarms, which significantly increases the true positive count and true negative count. The proposed OSOVOC algorithm produces negotiable false alarms, with performance reaching a F1-score of 100% during training phase and 99.87% during testing phase.
Co-Authors Poonkody Iruthayaraj