AI-Powered Anomaly Detection and Cybersecurity in Healthcare IoT with Fog-Edge

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 $67\times$ improvement over conventional cloud-based approaches. By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs ( $\text{ε}=1.0$–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.