Privacy-Preserving AI Analytics for Adaptive Intrusion Detection in Heterogeneous IoT Networks
Main article
Abstract
Heterogeneous Internet of Things (IoT) networks create a difficult intrusion-detection problem because traffic patterns, device capacities, privacy risks, and attack distributions differ across clients. Centralized intrusion detection systems can collect large volumes of data, but they also introduce latency, data-transfer exposure, and governance challenges. This article proposes a privacy-preserving AI analytics framework for adaptive intrusion detection in heterogeneous IoT networks. Inspired by recent work on federated intrusion detection, reinforcement-guided decision control, and fog-cloud security, the framework integrates local feature learning, federated aggregation, client-level adaptation, privacy-preserving update exchange, and explainable risk scoring. The study reconstructs a multi-domain evaluation design using general network, industrial IoT, medical IoT, vehicle-network, smart-grid, and large-scale intrusion datasets. Rather than focusing only on average accuracy, the analysis evaluates precision, recall, F1-score, false-positive rate, false-negative rate, latency, privacy cost, and scalability under IID and non-IID client settings. The results and discussion show that privacy-preserving AI analytics can support high-quality intrusion detection when decentralized training is combined with personalized aggregation, asynchronous updates, and adaptive decision thresholds. The article contributes a deployable analytics architecture and a practical evaluation logic for trustworthy cyber defense in distributed IoT environments.
