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Artificial Intelligence Based Security Improvement in Medical-IoT Health Care System Using Generative Deep Belief Neural Network

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Abstract

Artificial intelligence (AI) technology is the greatest option for medical applications since it improves data security and reliability. As a result, several AI-driven security procedures are implemented by standard IoT cloud framework initiatives. Medical Internet of Things (MIoT) devices have a role to play in meeting the urgent need for timely medical services, disease treatment, and monitoring of medical resources. Previous studies have demonstrated that health issues cannot be detected with sensitive features and cannot avoid data leakage, providing low security for sensitive data from medical IoT devices. So, to overcome the problems, an AI-based Generative Deep Belief Neural Network (AI -GDBN2) is to avoid the leakage of sensitive data and improve accuracy. We collect the dataset based on Medical-IoT features from the standard repository in the cloud; the initial step is preprocessing the data using normalization and cleaning, identifying missing values, eliminating inappropriate values, and reducing unscaled feature range weights that can provide the original dataset. The next step involves feature selection using the Fuzzy Recurrent Neural Network (FRNN) algorithm. This process utilizes the behavioral support factors of the features to determine their weights. Finally, the proposed AI-GDBN2 model selects the attributes based on their maximum feature weight. AI provides security for sensitive data and avoids data leakages. And the AI-GDBN2 is providing security based on the encryption method Advanced Honey Encryption Standard (AHES) to encrypt data using the maximum threshold values features and transmit paths. Next, using the randomly generated hash key, the AHES method encrypts the data. AI-GDBN2 classifies the results based on sensitive and non-sensitive feature values and avoids abnormal data leakages to improve security. So, the simulation result proposed method AI-GDBN2 shows an accuracy of 0.98%; the outcomes of both the suggested and previous approaches are validated and compared using various performance metrics.

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Data Availability

All the databases are freely available and cited in the manuscript.

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Acknowledgements

The authors acknowledged the SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India for supporting the research work by providing the facilities.

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Correspondence to J. Anupriya.

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Anupriya, J., Devi, R.R. Artificial Intelligence Based Security Improvement in Medical-IoT Health Care System Using Generative Deep Belief Neural Network. SN COMPUT. SCI. 5, 1027 (2024). https://doi.org/10.1007/s42979-024-03307-0

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