TBGWO-SE: a tournament binary grey wolf optimization-based heterogeneous stacked ensemble for intrusion detection
MD Moizuddin1, M. Victor Jose2 and S. Maria Celestin Vigila3
Research Scholar, Department of Computer Applications,Noorul Islam Centre for Higher Education, Kanyakumari,Tamil Nadu,India1
Professor, Department of Computer Science and Engineering,Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi,Tamil Nadu,India2
Associate Professor, Department of Information Technology,Noorul Islam Centre for Higher Education, Kanyakumari,Tamil Nadu,India3
Professor, Department of Computer Science and Engineering,Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi,Tamil Nadu,India2
Associate Professor, Department of Information Technology,Noorul Islam Centre for Higher Education, Kanyakumari,Tamil Nadu,India3
Corresponding Author : MD Moizuddin
Recieved : 01-September-2025; Revised : 23-June-2026; Accepted : 25-June-2026
Abstract
Protection of computing resources and software applications against malicious attacks remains a major security concern due to the increasing prevalence and sophistication of cyber threats. The complexity of distinguishing between benign and malicious behavior is further exacerbated by network traffic data characterized by numerous traffic features and diverse attack profiles. This paper presents a bio-inspired intrusion detection system (IDS), termed the tournament binary grey wolf optimization-stacked ensemble (TBGWO-SE). In the proposed framework, tournament binary grey wolf optimization (TBGWO) is employed to identify an optimal subset of features, while a stacked ensemble (SE) classifier is developed by integrating an adaptive Gaussian support vector machine (SVM) and a decision tree ensemble (DTE). For binary attack detection, the proposed IDS achieves classification accuracies of 99.67%, 99.66%, and 99.67% on the KDDTest+, KDDTest-21, and UNSW-NB15 datasets, respectively. For multi-class attack classification, it attains accuracies of 99.66%, 98.98%, and 95.32% on the corresponding datasets. The effectiveness of the proposed model is demonstrated through comprehensive performance evaluation and comparative analysis against representative state-of-the-art methods. Furthermore, the IDS exhibits strong generalization capability, indicating its potential for future extension toward the detection of previously unseen attacks.
Keywords
Intrusion detection system (IDS), Tournament binary grey wolf optimization (TBGWO), Feature selection, Stacked ensemble learning, Network traffic classification.
Cite this article
Moizuddin M, Jose MV, Vigila SMC. TBGWO-SE: a tournament binary grey wolf optimization-based heterogeneous stacked ensemble for intrusion detection. International Journal of Advanced Technology and Engineering Exploration. 2026;13(139):988-1013. DOI : 10.19101/IJATEE.2025.121221216
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