  Recent research works have proposed machine learning models for classifying
IoT devices connected to a network. However, there is still a practical
challenge of not having all devices (and hence their traffic) available during
the training of a model. This essentially means, during the operational phase,
we need to classify new devices not seen in the training phase. To address this
challenge, we propose ZEST -- a ZSL (zero-shot learning) framework based on
self-attention for classifying both seen and unseen devices. ZEST consists of
i) a self-attention based network feature extractor, termed SANE, for
extracting latent space representations of IoT traffic, ii) a generative model
that trains a decoder using latent features to generate pseudo data, and iii) a
supervised model that is trained on the generated pseudo data for classifying
devices. We carry out extensive experiments on real IoT traffic data; our
experiments demonstrate i) ZEST achieves significant improvement (in terms of
accuracy) over the baselines; ii) SANE is able to better extract meaningful
representations than LSTM which has been commonly used for modeling network
traffic.
