ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification
This addresses the practical challenge of classifying unseen IoT devices in network security, though it is an incremental method building on existing zero-shot learning approaches.
The paper tackles the problem of classifying IoT devices not seen during training by proposing ZEST, a zero-shot learning framework using self-attention, which achieves significant accuracy improvements over baselines on real IoT traffic data.
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.