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Mitigating IoT Enterprise Vulnerabilities Using Radio Frequency Security Architecture

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Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 544))

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Abstract

Internet of Things (IoT) as an emerging technology has metamorphized the use of smart devices, exchange of information and enhanced business processes in a ubiquitous and multi-faceted way. As Internet of Things (IoT) continues to gain tractable usage by billions of connected devices personal area network vulnerabilities, as identity and data theft, malware attacks and DDOS (Distributed Denial of Service) have become prevalent. In this study, we investigate the use of Radio Frequency (RF) technology mitigating IoT vulnerabilities. Deployment of secure RF network that enhances interoperability of IoT Devices by allocating unique ID (signature) is evaluated. This study further explores clustering of heterogenous networks of IoT devices using an intelligent Radio Frequency model that identifies labels and anonymizes Radio Waves shared by IoT devices within electromagnetic field. This vulnerability mitigation process encompasses use of Machine learning and neural networks classification to cluster heterogenous IoT and networks shared by IoT devices. The use of unique ID for clustered heterogenous is simulated in this study as against foreign and unauthorized radio frequency threats to paired IoT devices in a shared network.

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Author information

Authors and Affiliations

  1. University of The District of Columbia, Washington, DC, USA

    John Irungu & Anteneh Girma

Authors
  1. John Irungu
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  2. Anteneh Girma
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Corresponding author

Correspondence to John Irungu .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Irungu, J., Girma, A. (2023). Mitigating IoT Enterprise Vulnerabilities Using Radio Frequency Security Architecture. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_45

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