Scientific Reports· 2026Q1
Privacy-preserving intrusion detection in mobile edge networks via federated adaptive probabilistic stacking with clan–flock optimisation and explainable AI
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- Q1SCImago
- 2026year
Short summary
A novel federated intrusion detection system (FedAPS-CFO) achieves F1-scores up to 0.998 across four benchmark datasets (NSL-KDD, UNSW-NB15, CICIDS2017, CIC-IoT2023) while preserving data privacy and providing interpretability.
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Key points
- FedAPS-CFO achieves high F1-scores (0.951-0.998) on NSL-KDD, UNSW-NB15, CICIDS2017, and CIC-IoT2023 datasets.
- The system uses federated averaging to keep raw traffic local, enhancing privacy.
- An Adaptive Probabilistic Stacking ensemble fuses multiple models for robust detection.
- Clan-Flock Optimisation tunes fusion weights and thresholds, proving reliable for heterogeneous data.
- Explainable AI (SHAP/LIME) is integrated and validated for interpretability.
AI-generated from the title and abstract; the full text is not read.
Abstract
Mobile edge networks require intrusion detection systems (IDS) that are private, accurate and interpretable. We present FedAPS–CFO, a federated IDS coupling four mechanisms: prediction-level federated averaging (FedAvg), so raw traffic never leaves the edge; an Adaptive Probabilistic Stacking (APS) ensemble fusing a stacking meta-learner, Bayesian model averaging and a feature-conditioned ensemble; Clan–Flock Optimisation (CFO), a hybrid metaheuristic that tunes the fusion weights and decision threshold on inner validation splits; and SHapley Additive exPlanations (SHAP) with Local Interpretable Model-agnostic Explanations (LIME), validated quantitatively for stability, fidelity and faithfulness. Under stratified 10-fold cross-validation, FedAPS–CFO attains F1-scores of 0.997 on NSL-KDD, 0.951 on UNSW-NB15, 0.979 on CICIDS2017 and 0.998 on CIC-IoT2023, matching the strongest constituent ensemble on every dataset and significantly surpassing the remaining components (Wilcoxon signed-rank, Holm-corrected). In federations of up to 100 clients with Dirichlet label skew, FedAPS–CFO sustains detection within 1.2 F1 points of its ideal operating point — up to 46 points above its federated base learners — showing that the trained fusion absorbs client heterogeneity. Under matched budgets, CFO is the most reliable optimiser for the tuning task. Adaptive posterior fusion thus offers a practical route to accurate, interpretable, privacy-preserving intrusion detection at the network edge.
The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗
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Field: Computer Networks and Communications
Computer Networks and CommunicationsComputer Science