A hybrid ensemble framework with particle swarm optimization for network anomaly detection
- Title
- A hybrid ensemble framework with particle swarm optimization for network anomaly detection
- Creator
- Verma, Narinder; Kumar, Neerendra; Kumar, Gourav; Singh, Kuljeet
- Description
- The increasing complexity of cyber threats necessitates the development of a robust and adaptive Intrusion Detection System (IDS) capable of safeguarding network infrastructures. Traditional IDS approaches often struggle to detect sophisticated attacks due to their reliance on predefined patterns. We propose an adaptive particle swarm optimization (PSO)-optimized ensemble learning framework tailored to address these challenges in modern IDS applications. Our approach leverages the NSL-KDD and CICIDS datasets to ensure the IDS is trained and evaluated on data reflecting current network behaviours and threat landscapes. We evaluate multiple machine learning models, including Decision Trees (DT), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), and an ensemble of these models for both binary and multi-class classification tasks. By incorporating adaptive mechanisms within the PSO algorithm, our framework dynamically adjusts hyperparameters during optimization, enhancing model robustness and convergence speed. The proposed framework is also benchmarked against state-of-the-art IDS approaches, including ASRL and PSOGSA. Empirical evaluations demonstrate that the ensemble model achieves superior detection accuracy and reduced false positive rates, thereby advancing the efficacy of intrusion detection methodologies. The Author(s) 2025.
- Source
- Discover Applied Sciences;Volume;7;Issue;8;Article No.;921;
- Date
- 01-01-2025
- Publisher
- Springer Nature
- Subject
- Ensemble learning; Intrusion detection system (IDS); Network security; Particle swarm optimization (PSO)
- Coverage
- Verma N., Department of Computer Science and Information Technology, Central University of Jammu, Jammu, 181143, India, School of Computer Science and Engineering, IILM University, Greater Noida, 201306, India; Kumar N., Department of Computer Science and Information Technology, Central University of Jammu, Jammu, 181143, India; Kumar G., Department of Computer Science and Engineering, Central University of Jammu, Jammu, 181143, India; Singh K., Department of Computer Science, School of Sciences, Christ University, Delhi-NCR, 201003, India
- Rights
- All Open Access; Gold Open Access; Green Open Access
- Relation
- ISSN: 30049261;
- Format
- online
- Language
- English
- Type
- Article
Collection
Citation
Verma, Narinder; Kumar, Neerendra; Kumar, Gourav; Singh, Kuljeet, “A hybrid ensemble framework with particle swarm optimization for network anomaly detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22125.