CAUTION: A Robust WiFi-Based Human Authentication System via Few-Shot Open-Set Recognition

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CAUTION: A Robust WiFi-Based Human Authentication System via Few-Shot Open-Set Recognition
Title:
CAUTION: A Robust WiFi-Based Human Authentication System via Few-Shot Open-Set Recognition
Journal Title:
IEEE Internet of Things Journal
Publication Date:
03 March 2022
Citation:
Wang, D., Yang, J., Cui, W., Xie, L., & Sun, S. (2022). CAUTION: A Robust WiFi-Based Human Authentication System via Few-Shot Open-Set Recognition. IEEE Internet of Things Journal, 9(18), 17323–17333. https://doi.org/10.1109/jiot.2022.3156099
Abstract:
Existing channel-state information (CSI)-based human authentication systems in the literature require a large amount of CSI data to train deep neural network (DNN) models and are ineffective for unknown intruder detection. To address this issue, we propose a CSI-based human authentication system (CAUTION) which is able to learn distinctive gait features of different users through CSI data to perform human authentication in this article. By taking advantage of few-shot learning, CAUTION is able to construct an accurate user identification model with a very limited number of CSI training data. By converting the CSI samples into low-dimensional representations on the feature plane, it computes central points for different users as their CSI profiles and introduces an intruder threshold to measure whether the CSI data matches one of the user classes by a margin. The intruder threshold is able to be optimized without any intruders’ data. CAUTION does not require a large number of training data and provides an effective way to train the system for unknown intruder detection. We have tested CAUTION at different places and compared it with state-of-the-art CSI-based authentication systems. The experimental results demonstrate that CAUTION is able to perform accurate human authentication with a limited amount of CSI training data (one-fifth of data needed by compared systems) and outperforms the compared human authentication systems.
License type:
Publisher Copyright
Funding Info:
This research is supported by Agency for Science, Technology and Research (Singapore) under AGS scholarship
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2327-4662
2372-2541
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