A Comprehensive Study on Machine Learning Algorithms for Wireless Sensor Network Security

Humaira, Fozilatoon; Islam, Sanju; Nur, Fernaz Narin; Hussain, Kazi Anowar

doi:10.1109/icccnt49239.2020.9225659

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…In all the surveys discussed above, little-to-no emphasis is placed on how the components of an RFML ecosystem might impact the works cited, with the primary focus being the algorithms and applications of interest. A few more unique surveys have also examined individual components of an RFML ecosystem including dataset generation considerations using tools such as GNU Radio [18] and security and privacy challenges faced in cognitive wireless sensor networks [19], [20]. Additionally, though discussed in the context of CR, [21] and [22] also discuss operational considerations for using RFML in a military setting and solutions for combating practical imperfections encountered in CR system (i.e.…”

Section: Prior Work and Contributionsmentioning

confidence: 99%

An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness

Wong

Clark

Flowers

et al. 2021

IEEE Open J. Commun. Soc.

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While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to wireless communications, a field loosely termed Radio Frequency Machine Learning (RFML). In particular, recent research has shown DL to be an enabling technology for Cognitive Radio (CR) applications as well as a useful tool for supplementing expertly defined algorithms for spectrum awareness applications such as signal detection, estimation, and classification. A major driver for the usage of RFML is that little, to no, a priori knowledge of the intended spectral environment is required, given that there is an abundance of representative raw Radio Frequency (RF) data to facilitate training and evaluation. However, in addition to this fundamental need for sufficient data, there are other key considerations, such as trust, security, and hardware requirements, that must be taken into account before deploying RFML systems in real-world wireless communication applications that largely go unaddressed in the current literature. This paper examines the prior works related to these major research considerations, with focus on the dependencies between them and factors unique to the RFML space.

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