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

Chung, Antony (2020) PhyForm - A cloud SDR framework for security research supporting machine learning of wireless IoT signal data sets. In: Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020), 2020-02-172020-02-19.

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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.

Item Type:
Contribution to Conference (Poster)
Journal or Publication Title:
Seventeenth International Conference on Embedded Wireless Systems and Networks (EWSN 2020)
ID Code:
179211
Deposited By:
Deposited On:
24 Nov 2022 14:00
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2022 14:00