Intrusion Detection using Federated Attention Neural Network for Edge Enabled Internet of Things

Abstract

Edge nodes, which are expected to grow into a multi-billion-dollar market, are essential for detection against a variety of cyber threats on Internet-of-Things endpoints. Adopting the current network intrusion detection system with deep learning models (DLM) based on FedACNN is constrained by the resource limitations of this network equipment layer. We solve this issue by creating a unique, lightweight, quick, and accurate edge detection model to identify DLM-based distributed denial service attacks on edge nodes. Our approach can generate real results at a relevant pace even with limited resources, such as low power, memory, and processing capabilities. The Federated Convolution Neural Network (FedACNN) deep learning method uses attention mechanisms to minimise communication delay. The developed model uses a recent cybersecurity dataset deployed on an edge node simulated by a Raspberry Pi (UNSW 2015). Our findings show that, compared to traditional DLM methodologies, our model retains a high accuracy rate of about 99%, even with decreased CPU and memory resource use. Also, it is about three times smaller in volume than the most advanced model while requiring a lot less testing time.