Abstract
For pervasive applications of the artificial neural networks (ANN), the autonomous control usually affects the overall processing and analysis of data in the healthcare systems. Though, involvement of several malicious activities by the intruders may drastically affect the overall communication system. Recently, more and more engineers have applied a trusted and secure Artificial Internet of Things (AIoT) healthcare system is used to analyze and process the accurate control for overall benefits. However, the lack of security and trust in IoT devices results in accidental control of risks. The aim of this chapter is to propose an ANN-based secure network to analyze the legitimacy of IoT devices by categorizing through back propagation and Bayesian rule schemes. The proposed system can efficiently recognize the illegal activity of malicious IoT devices used to record, manage, and store the sensitive information of healthcare centers. The proposed phenomenon is proposed over various security metrics against conventional scheme. Further, we have discussed the Software-Defined Networks (SDNs) architectures that provide better solutions by removing the decision making capabilities from intermediate nodes in the network.
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Rathee, G. (2022). Trusted Mechanism Using Artificial Neural Networks in Healthcare Software-Defined Networks. In: Aujla, G.S., Garg, S., Kaur, K., Sikdar, B. (eds) Software Defined Internet of Everything. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-89328-6_12
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DOI: https://doi.org/10.1007/978-3-030-89328-6_12
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