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PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets

Research output: Contribution to conference - Without ISBN/ISSN › Poster › peer-review

Published

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PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets. / Chung, Antony.
2020. Poster session presented at Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020), Lyon, France.

Research output: Contribution to conference - Without ISBN/ISSN › Poster › peer-review

Harvard

Chung, A 2020, 'PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets', Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020), Lyon, France, 17/02/20 - 19/02/20. <https://dl.acm.org/doi/10.5555/3400306.3400337>

APA

Chung, A. (2020). PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets. Poster session presented at Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020), Lyon, France. https://dl.acm.org/doi/10.5555/3400306.3400337

Vancouver

Chung A. PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets. 2020. Poster session presented at Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020), Lyon, France.

Author

Chung, Antony. / PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets. Poster session presented at Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020), Lyon, France.

Bibtex

@conference{c0df87ceaf27457082332c3a933176c3,
title = "PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets",
abstract = "Software defined radio (SDR) enables the use of digital signal processing (DSP) to identify IoT security issues based on waveform analysis. Such research requires the handling, processing and interaction with large data sets of digitised RF. Those supporting activities are a high overhead.An extensible framework is introduced for the curation, filtering, pre-processing, and analysis tasks associated with RF data sets in machine learning and IoT research. It provides a web interface, API, SigMF data sharing and integration with GNU Radio. The aim is improved data set and algorithm collaboration. A LoRa example provides context.",
author = "Antony Chung",
year = "2020",
language = "English",
note = "Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020), EWSN ; Conference date: 17-02-2020 Through 19-02-2020",
url = "https://ewsn2020.conf.citi-lab.fr/",

}

RIS

TY - CONF

T1 - PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets

AU - Chung, Antony

N1 - Conference code: 17

PY - 2020

Y1 - 2020

N2 - Software defined radio (SDR) enables the use of digital signal processing (DSP) to identify IoT security issues based on waveform analysis. Such research requires the handling, processing and interaction with large data sets of digitised RF. Those supporting activities are a high overhead.An extensible framework is introduced for the curation, filtering, pre-processing, and analysis tasks associated with RF data sets in machine learning and IoT research. It provides a web interface, API, SigMF data sharing and integration with GNU Radio. The aim is improved data set and algorithm collaboration. A LoRa example provides context.

AB - Software defined radio (SDR) enables the use of digital signal processing (DSP) to identify IoT security issues based on waveform analysis. Such research requires the handling, processing and interaction with large data sets of digitised RF. Those supporting activities are a high overhead.An extensible framework is introduced for the curation, filtering, pre-processing, and analysis tasks associated with RF data sets in machine learning and IoT research. It provides a web interface, API, SigMF data sharing and integration with GNU Radio. The aim is improved data set and algorithm collaboration. A LoRa example provides context.

M3 - Poster

T2 - Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020)

Y2 - 17 February 2020 through 19 February 2020

ER -