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Radio frequency identification (RFID) technology has already demonstrated its use. RFID is used in many productions for different applications, for example, apparatus chasing, personal and vehicle access panels, logistics, baggage, and safety items in departmental stores. The main benefits of RFID are optimizing resources, quality customer service, improved accuracy, and efficient business and healthcare procedures. In addition, RFID can help to recognize appropriate information and help advance the probability of objects for certain functions. Nevertheless, RFID components need to be studied for use in healthcare. Antennas, tags, and readers are the main components of RFID. The study of these elements provides an understanding of the usage and integration of these components in healthcare environments. The security of the patient is now a global alarm for public health, particularly among older people who need integrated and technologically integrated physiological health monitoring systems to monitor medical needs and manage them. This paper proposes using Internet of Things (IoT) and RFID tags as an effective healthcare monitoring system. In this method, we utilize RFID dual-band protocols that are useful for identifying individual persons and are used to monitor body information using high frequency. The patient’s physiological data are monitored and collected by sensors to recognize the patient, using an RFID tag. The IoT-based RFID healthcare provides the elderly and people with physiological information. The aim is also to secure patient health records using the signing algorithm based on the hyperelliptic curve (HEC) and to provide the physician with access to health information for patients. Furthermore, the confidentiality of the medical records for patients of variable length is provided. The evaluation reveals the algorithm proposed for optimum health care with different genus curves.

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The internet, like automated tools, has grown to better our daily lives. Interacting IoT products and cyber-physical systems. Generative Adversarial Network's (GANs') generator and discriminator may have different inputs, allowing feedback in supervised models. AI systems use neural networks, and adversarial networks analyse neural network feedback. Cyber-physical production systems (CPPS) herald intelligent manufacturing . CPPS may launch cross-domain attacks since the virtual and real worlds are interwoven. This project addresses enhanced Cyber-Physical System(CPS) feedback structure for Denial-of-Service (DoS) defence . Comparing sensor-controller and controller-to-actuator DoS attack channels shows a swapping system modelling solution for the CPS's complex response feedback. Because of the differential in bandwidth between the two channels and the suspects' limited energy, one person can only launch so many DoS assaults. DoS attacks are old and widespread. Create a layered switching paradigm that employs packet-based transfer techniques to prevent assaults. The discriminator's probability may be used to assess whether feedback samples came from real or fictional data. Cognitive feedback can assess GA feedback data.

Secure data transfer in mobile ad hoc network (MANET) against malicious attacks is of immense importance. In this paper, we propose a new enhanced trust model for securing the MANET using trust-based scheme that uses both blind trust and referential trust. In order to do this, the trust relationship function has to be integrated with the dynamic source routing (DSR) protocol for making the protocol more secure. We thoroughly analyze the DSR protocol and generate the performance matrices for the data pertaining to packets sent, packets received, packets loss, and throughput. We also analyze the outcome attained from the improvised trust establishment scheme by using the three algorithm implementations in NS2 simulator for detecting and preventing various types of attacks.