A Hybrid Intrusion Detection Framework for Energy-Constrained IOT Devices

The widespread adoption of Internet of Things (IoT) devices has enabled transformative applications acrossindustries, yet it has also introduced significant security vulnerabilities, especially in energy-constrainedenvironments. Conventional intrusion detection systems (IDS) often impose high computational and energyoverheads, limiting their applicability in IoT networks. This paper presents a hybrid intrusion detection frameworkspecifically designed for energy-limited IoT devices, combining signature-based detection, anomaly detection, andlightweight machine learning techniques. The framework ensures effective detection without sacrificing devicelongevity by utilizing cloud-based resources for advanced threat intelligence and edge computing for real-time localanalysis. Comparing the suggested framework to current IDS techniques, experimental assessments on benchmarkIoT datasets show that it delivers better detection accuracy, lower false positive rates, and improved energyconsumption. The findings show that a hybrid, energy-aware IDS can successfully protect IoT networks from avariety of cyberthreats, providing a scalable and useful solution for both consumer and industrial IoT deployments.