Real-Time Jamming Detection in Wireless IoT Networks

IoT-based networks are vulnerable to jamming attacks due their large-scale deployment and shared communication environment. Resource constraints the low computational power of IoT devices make it harder implement high-performance ML-based architectures for detection. In this work, effects on a Wi-Fi network presented novel real-time detection mechanism is devised which can identify multiple channels in 2.4 GHz bandwidth simultaneously. The experiments conducted lab environment by generating with Software Defined Radio. Certain QoS parameters an end-to-end wireless system collected during normal operating conditions attacks. implemented employing communication. proposed method has accuracy 99% zero false alarms. It benefits from profile detect requires minimal resources regarding memory CPU usage makes low-cost easily deployable solution devices.