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Publication: IIP Proceedings Year: 2022, Month: November Page No: 16-24, ISSN/ISBN: 978-93-95632-81-2, DOI/Link: https://rsquarel.org/assets/docupload/rsl202350CD4ADD980B688.pdf
A STUDY ON REINFORCEMENT LEARNING TO KEEP DEVICES SECURED IN COGNITIVE RADIO NETWORKS
Area/Stream: Artificial Intelligence, Authors: Galiveeti Poornima, Sudha Y Keywords: Cognitive radio; Radio Transmission; Reinforcement learning; trust; reputation; security; cognitive radio networks Book Name /series: Futuristic Trends in Artificial Intelligence, Volume 2, Book 17, Part 1, Chapter 3Publication: IIP Proceedings Year: 2022, Month: November Page No: 16-24, ISSN/ISBN: 978-93-95632-81-2, DOI/Link: https://rsquarel.org/assets/docupload/rsl202350CD4ADD980B688.pdf
Abstract:
Cognitive Radio is a new generation of radio that uses artificial intelligence to augment the capabilities of the transmission medium. Wireless devices that are able to learn from their surroundings are likewise susceptible to being taught things by potentially harmful components of their surroundings. Since malevolent nodes can easily intercept transmissions on wireless networks because of their broadcast nature, these networks create a greater security risk for data transmission. There are a wide range of security threats that can interrupt a wireless network because of the inherent vulnerability to interception. Reinforcement learning, or RL, is an approach to artificial intelligence that has been implemented to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This has been made possible through the application of an artificial intelligence technique known as reinforcement learning.
Cite this: Galiveeti Poornima, Sudha Y,"A STUDY ON REINFORCEMENT LEARNING TO KEEP DEVICES SECURED IN COGNITIVE RADIO NETWORKS", Futuristic Trends in Artificial Intelligence, Volume 2, Book 17, Part 1, Chapter 3 , November, 2022, 16-24, 978-93-95632-81-2, https://rsquarel.org/assets/docupload/rsl202350CD4ADD980B688.pdf
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