UNSW HomeNet: A network traffic flow dataset for AI-based smart home device classification

The emergence of the Internet of Things (IoT) has introduced a variety of devices into smart homes, making smart home networks increasingly complex and insecure. However, many IoT device manufacturers prioritize functionality, time-to-market, and performance over security, leaving IoT devices and networks vulnerable. Automatic device classification techniques are crucial for applying various network management approaches to ensure both performance and security. Despite the considerable research effort devoted to device classification, very few datasets are publicly available for in-depth investigation. This paper identifies the currently available public datasets for smart home device classification and highlights their limitations. These limitations encouraged us to develop a new, large-scale network traffic flow dataset for AI-Based smart home device classification dataset comprising more than 200 million data points stemming from 105 different IoT and non-IoT devices. This dataset is now publicly available to the research community, and in this paper we present and describe its properties. Furthermore, we evaluated the effectiveness of different Machine Learning algorithms in classifying these devices. Our results indicate that the Random Forest algorithm achieves the highest accuracy at 0.906 with recall, precision, and F1 scores of 0.877, 0.901, and 0.887, respectively. Finally, we investigated the importance of the features and found that only 12 features are largely responsible for the observed levels of accuracy. • Current datasets for smart home device classification have limited use in practice. • We developed and characterized the largest dataset to date with > 200 M datapoints. • A key property of the dataset is its diversity. • The dataset has 88 features from 105 devices spanning 23 different types. • Random Forest performs best on this dataset with metrics around 90%.