Abstract
Deep Learning (DL) has been utilized pervasively in the Internet of
Things (IoT). One typical application of DL in IoT is device
identification from wireless signals, namely Non-cryptographic Device
Identification (NDI). However, learning components in NDI systems have
to evolve to adapt to operational variations, such a paradigm is termed
as Incremental Learning (IL). Various IL algorithms have been proposed
and many of them require dedicated space to store the increasing amount
of historical data, and therefore, they are not suitable for IoT or
mobile applications. However, conventional IL schemes can not provide
satisfying performance when historical data are not available. In this
paper, we address the IL problem in NDI from a new perspective, firstly,
we provide a new metric to measure the degree of topological maturity of
DNN models from the degree of conflict of class-specific fingerprints.
We discover that an important cause for performance degradation in IL
enabled NDI is owing to the conflict of devices’ fingerprints. Second,
we also show that the conventional IL schemes can lead to low
topological maturity of DNN models in NDI systems. Thirdly, we propose a
new Channel Separation Enabled Incremental Learning (CSIL) scheme
without using historical data, in which our strategy can automatically
separate devices’ fingerprints in different learning stages and avoid
potential conflict. Finally, We 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. Data and code
available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) and
\url{https://github.com/pcwhy/CSIL}}.