Secure IoT Healthcare Architecture with Deep Learning‐Based Access Control System

Thilagam, K.; Beno, A.; Lakshmi, M. Prasanna; Wilfred, C. Bazil; George, Santhi M.; Karthikeyan, M.; Vijayakumar, P.; Ramesh, C.; Karunakaran, Prabakaran

doi:10.1155/2022/2638613

Cited by 26 publications

(5 citation statements)

References 29 publications

(31 reference statements)

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“…A battery for uninterrupted data transmission and reception fuels each sensor component. Each sensor node battery has an initial energy capacity of one thousand joules [15][16][17][18][19][20].…”

Section: Proposed Methodologymentioning

confidence: 99%

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Mehdy¹,

Qasim²,

A.Habeeb³

et al. 2023

PEN

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The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use.

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Section: Proposed Methodologymentioning

confidence: 99%

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Mehdy¹,

Qasim²,

A.Habeeb³

et al. 2023

PEN

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“…Properties such as secrecy, integrity, authorization, privacy, permission, and availability must all be ensured in order to ensure security in the IoT. Following is a summary of specific recent articles related to this research: Thilagam, K. et al [ 30 ] offered IoT-based deep learning techniques based on privacy protection and a data analytics system. The health-related data are examined in the cloud using a convolutional neural network (CNN), free of user privacy data.…”

Section: Related Workmentioning

confidence: 99%

Managing Security of Healthcare Data for a Modern Healthcare System

Almalawi

Khan

Alsolami

et al. 2023

Sensors

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The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals’ access to cloud-based patient-sensitive data more securely. The experiment’s findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.

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“…The proposed system could be used for the early diagnosis and treatment of DR, thereby improving patient outcomes. As well, Thilagam et al (38) presented a secure Internet of Things (IoT) healthcare architecture with a deep learning-based access control system. The proposed system is designed to ensure that only authorized personnel can access the sensitive medical information stored in IoT devices.…”

Section: Figurementioning

confidence: 99%

Medical image analysis using deep learning algorithms

Li,

Jiang,

Zhang

et al. 2023

Front. Public Health

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In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

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Secure IoT Healthcare Architecture with Deep Learning‐Based Access Control System

Cited by 26 publications

References 29 publications

A Deep learning approach for trust-untrust nodes classification problem in WBAN

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Managing Security of Healthcare Data for a Modern Healthcare System

Medical image analysis using deep learning algorithms

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