TY  - EJOU
AU  - Al-Quayed, Fatima 

TI  - AI-Powered Anomaly Detection and Cybersecurity in Healthcare IoT with Fog-Edge
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 1
SN  - 1526-1506

AB  - The rapid proliferation of Internet of Things (IoT) devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative, distributed architectural solutions. This paper proposes FE-ACS (Fog-Edge Adaptive Cybersecurity System), a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge, fog, and cloud layers to optimize security efficacy, latency, and privacy. Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923, while maintaining significantly lower end-to-end latency (18.7 ms) compared to cloud-centric (152.3 ms) and fog-only (34.5 ms) architectures. The system exhibits exceptional scalability, supporting up to 38,000 devices with logarithmic performance degradation—a <mml:math id="mml-ieqn-1"><mml:mn>67</mml:mn><mml:mo>×</mml:mo></mml:math> improvement over conventional cloud-based approaches. By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs (<mml:math id="mml-ieqn-2"><mml:mtext>ε </mml:mtext><mml:mo>=</mml:mo><mml:mn>1.0</mml:mn></mml:math>–1.5), FE-ACS maintains 90%–93% detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data. Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference. In healthcare risk assessment, FE-ACS demonstrates robust operational viability with low patient safety risk (14.7%) and high system reliability (94.0%). The proposed framework represents a significant advancement in distributed security architectures, offering a scalable, privacy-preserving, and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
KW  - AI-powered anomaly detection; healthcare IoT; fog computing; cybersecurity; intrusion detection

DO  - 10.32604/cmes.2025.074799