  The Internet of Things (IoT) provides applications and services that would
otherwise not be possible. However, the open nature of IoT make it vulnerable
to cybersecurity threats. Especially, identity spoofing attacks, where an
adversary passively listens to existing radio communications and then mimic the
identity of legitimate devices to conduct malicious activities. Existing
solutions employ cryptographic signatures to verify the trustworthiness of
received information. In prevalent IoT, secret keys for cryptography can
potentially be disclosed and disable the verification mechanism.
Non-cryptographic device verification is needed to ensure trustworthy IoT. In
this paper, we propose an enhanced deep learning framework for IoT device
identification using physical layer signals. Specifically, we enable our
framework to report unseen IoT devices and introduce the zero-bias layer to
deep neural networks to increase robustness and interpretability. We have
evaluated the effectiveness of the proposed framework using real data from
ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in
aviation. The proposed framework has the potential to be applied to accurate
identification of IoT devices in a variety of IoT applications and services.
Codes and data are available in IEEE Dataport.
