Network Traffic Flow Based Machine Learning Technique for IoT Device Identification

Security and privacy issues are being raised as smart systems are integrated into our daily lives. New security issues have emerged with several new vendors that develop the Internet of Things (IoT) products. The contents and patterns of network traffic will expose vulnerable IoT devices to intruders. New methods of network assessment are needed to evaluate the type of network connected IoT devices. IoT device recognition would provide a comprehensive structure for the development of stable IoT networks. This paper chooses a machine learning technique to identify IoT devices linked to the network by analyzing network flow sent and received. To generate network traffic data, we have developed a dataset adapted from the IoT23 Pcap files to experiment with a smart home network. We have created a model to identify the IoT device based on network traffic analysis. We evaluate our proposed model via full features dataset, reduces features dataset, and flow-based features dataset. This paper focuses on using flow-based features to identify the IoT device connected to the network. Our proposed scheme results in 100% precision, precision, recall, and F score via a full features dataset, reduced features dataset, and flow-based features dataset. Through evaluations using our produced dataset, we demonstrate that the proposed model can accurately classify IoT devices.