  Internet of Things (IoT) has brought along immense benefits to our daily
lives encompassing a diverse range of application domains that we regularly
interact with, ranging from healthcare automation to transport and smart
environments. However, due to the limitation of constrained resources and
computational capabilities, IoT networks are prone to various cyber attacks.
Thus, defending the IoT network against adversarial attacks is of vital
importance. In this paper, we present a novel intrusion detection approach for
IoT networks through the application of a deep learning technique. We adopt a
cutting-edge IoT dataset comprising IoT traces and realistic attack traffic,
including denial of service, distributed denial of service, reconnaissance and
information theft attacks. We utilise the header field information in
individual packets as generic features to capture general network behaviours,
and develop a feed-forward neural networks model with embedding layers (to
encode high-dimensional categorical features) for multi-class classification.
The concept of transfer learning is subsequently adopted to encode
high-dimensional categorical features to build a binary classifier. Results
obtained through the evaluation of the proposed approach demonstrate a high
classification accuracy for both binary and multi-class classifiers.
