Anomaly Detection Tools for the Lifecycle Security of Smart Systems
Diego Argüello Ron, Armando Aguayo-Mendoza, Óscar García-Perales, Antonis Mpantis, George Triantafyllou, Norbert Goetze, Rosella Omana Mancilla · 2025
The explosive growth of the Internet of Things (IoT) demands security mechanisms that adapt to emerging threats. Telemetry meets this need by fusing federated learning, explainable AI, and privacy-preserving analytics into a multi-layer monitoring framework. Lightweight agents such as r-Monitoring impose only 0.27 % CPU overhead at device level, while the BACON federated anomaly detector safeguards system traffic. On a 21-sensor industrial robot, TELEMETRY's Nokia pipeline flagged subtle speed anomalies with 73% accuracy; within the NF-ToN-IoT corpus, BACON differentiated benign from malicious flows with 96% accuracy and markedly fewer false positives than signature baselines. The Misuse Detection Toolkit ensemble further achieved 97.7% validation accuracy across 50 training epochs, underscoring the framework's adaptability. Together, these layers cut detection latency and reduce on-device resource use, illustrating how ML-driven, federated monitoring can harden next-generation IoT deployments. Ongoing work explores graph-based analytics, adaptive models, and more scalable federation schemes.