earticle

영어

In this paper, a machine learning-based technique is presented for detecting compromised IoT devices in edge computing networks. The model profiles device behavior using parameters such as CPU usage, network traffic, and power data, detecting anomalies that suggest an attack may be in progress. The lightweight framework can achieve high detection accuracy at low computational cost and is capable of processing in realtime.