Identification of IoT Devices Based on Feature Vector Split

Device identification and management effectively prevent security issues caused by the massive access of Internet of Things (IoT) devices. However, when there is access of new devices and firmware upgrade of known devices in IoT, frequent model re-training based on multi-classing becomes difficult, which problem could be solved by developing a separate identification model for each device, but the identification accuracy is usually low due to model overlapping. In this article, we propose a method of feature vector splitting to reduce the overlap between models and develop a Sub-Vector Joint Model Group based on K-means algorithm, which can detect normal network behavior of each device and classify them in real-time. We evaluate the efficacy of our scheme with public dataset, and the result shows that the method we proposed could reach an overall accuracy of over 98%, and effectively reduce the training time and storage cost of the model simultaneously.