  With the widespread adoption of Internet of Things (IoT), billions of
everyday objects are being connected to the Internet. Effective management of
these devices to support reliable, secure and high quality applications becomes
challenging due to the scale. As one of the key cornerstones of IoT device
management, automatic cross-device classification aims to identify the semantic
type of a device by analyzing its network traffic. It has the potential to
underpin a broad range of novel features such as enhanced security (by imposing
the appropriate rules for constraining the communications of certain types of
devices) or context-awareness (by the utilization and interoperability of IoT
devices and their high-level semantics) of IoT applications. We propose an
automatic IoT device classification method to identify new and unseen devices.
The method uses the rich information carried by the traffic flows of IoT
networks to characterize the attributes of various devices. We first specify a
set of discriminating features from raw network traffic flows, and then propose
a LSTM-CNN cascade model to automatically identify the semantic type of a
device. Our experimental results using a real-world IoT dataset demonstrate
that our proposed method is capable of delivering satisfactory performance. We
also present interesting insights and discuss the potential extensions and
applications.
