Anomaly Detection Tools for the Lifecycle Security of Smart Systems

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.