Gizliliği Koruyan Saldırı Tespit Sistemi: Mobil Kenar Ağlarında Federasyonlu Adaptif Olasılıksal Yığma, Klan-Sürü Optimizasyonu ve Açıklanabilir Yapay Zeka ile

Özet (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.

Yazarların özeti; kaynağından alınmıştır. Scientific Reports, 2026 · DOI ↗