Abstract
Due to the sheer number and heterogeneity of IoT devices, it is not possible to secure the IoT ecosystem using traditional endpoint and network security solutions. To address the requirements of securing IoT devices in edge home networks, this paper proposed a strategy capable of securing the network against most malicious activity in real-time. Firstly, we use an edge smart gateway, which is deployed on a Raspberry-Pi, runs an SDN controller and Open vSwtich (OVS) to perform traffic monitoring, anomaly detection, and traffic filtering. Then, with limited resources available on edge gateway, we use a lightweight machine-learning algorithm to classify device to device traffic and identify if there is a network intrusion. The classification model extracts features of network traffic, trained by classic supervised learning method-Decision Tree J48, then distinguish between benign and malicious traffic patterns observed in the network. Simulation results show that the model has high accuracy of intrusion and can effectively ensure security of home IoT interactions.
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