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Using Near-Field Electromagnetic Side Channels for Efficiently Fingerprinting Wireless Modules

This dataset is part of the article "Using Near-Field Electromagnetic Side Channels for Efficiently Fingerprinting Wireless Modules" intended for submission to IEEE PAINE 2024. 
Abstract: We introduce a fingerprinting method for wireless transceivers based on near-field electromagnetic (EM) side-channel measurements. The fingerprints are obtained from measured EM signals emitted by mixed-signal circuit operations in integrated wireless modules during reception of beacon frames in the IEEE 802.11n wireless standard. The method can identify 8 Wi-Fi modules with device-dependent features derived from a multi-class linear discriminant analysis classifier. We additionally identify modules measured three weeks after measurements of the training set. 

Description: Raw data from this project include voltage waveforms versus time, recorded using a real-time scope, in time-domain at multiple spatial locations over a chip's surface. Raw data are stored as csv files.

About this Dataset

Updated: 2026-09-19
Metadata Last Updated: 2024-07-19 00:00:00
Date Created: N/A
Data Provided by:
Dataset Owner: N/A

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Table representation of structured data
Title Using Near-Field Electromagnetic Side Channels for Efficiently Fingerprinting Wireless Modules
Description This dataset is part of the article "Using Near-Field Electromagnetic Side Channels for Efficiently Fingerprinting Wireless Modules" intended for submission to IEEE PAINE 2024.  Abstract: We introduce a fingerprinting method for wireless transceivers based on near-field electromagnetic (EM) side-channel measurements. The fingerprints are obtained from measured EM signals emitted by mixed-signal circuit operations in integrated wireless modules during reception of beacon frames in the IEEE 802.11n wireless standard. The method can identify 8 Wi-Fi modules with device-dependent features derived from a multi-class linear discriminant analysis classifier. We additionally identify modules measured three weeks after measurements of the training set.  Description: Raw data from this project include voltage waveforms versus time, recorded using a real-time scope, in time-domain at multiple spatial locations over a chip's surface. Raw data are stored as csv files.
Modified 2024-07-19 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:[email protected]
Keywords Near-field metrology; side-channel security; wireless communication protocols; wireless systems; fingerprinting
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    "title": "Using Near-Field Electromagnetic Side Channels for Efficiently Fingerprinting Wireless Modules",
    "description": "This dataset is part of the article \"Using Near-Field Electromagnetic Side Channels for Efficiently Fingerprinting Wireless Modules\" intended for submission to IEEE PAINE 2024.\u00a0\nAbstract: We introduce a fingerprinting method for wireless transceivers based on near-field electromagnetic (EM) side-channel measurements. The fingerprints are obtained from measured EM signals emitted by mixed-signal circuit operations in integrated wireless modules during reception of beacon frames in the IEEE 802.11n wireless standard. The method can identify 8 Wi-Fi modules with device-dependent features derived from a multi-class linear discriminant analysis classifier. We additionally identify modules measured three weeks after measurements of the training set.\u00a0\n\nDescription: Raw data from this project include voltage waveforms versus time, recorded using a real-time scope, in time-domain at multiple spatial locations over a chip's surface. Raw data are stored as csv files.",
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            "description": "Snippet of the window for beacon capture after network connection. xdata is time in milliseconds. ydata is probe voltage in milliVolts.\n\n",
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            "description": "Comparison of device ID accuracy for different bin-sizes. xdata is Bin size in KHz and ydata is accuracy.\n    Rows indicate max accuray for day1 and week3, average accuracy for day1 and week3, and min accuracy for day1 and week3 respectively.",
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            "description": "Frequency response for 5 devices at harmonic of CPU clock frequency. xdata is Frequency in MHz.  ydata is probe voltage in milliVolts. Each column of data corresponds to a new device.",
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    "theme": [
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}