ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911755646533632 |
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| author | Wu, Binghui Gysel, Philipp Divakaran, Dinil Mon Gurusamy, Mohan |
| author_facet | Wu, Binghui Gysel, Philipp Divakaran, Dinil Mon Gurusamy, Mohan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_08036 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification Wu, Binghui Gysel, Philipp Divakaran, Dinil Mon Gurusamy, Mohan Networking and Internet Architecture Cryptography and Security Machine Learning 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. |
| title | ZEST: Attention-based Zero-Shot Learning for Unseen IoT Device Classification |
| topic | Networking and Internet Architecture Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2310.08036 |