FBASHI: Fuzzy and Blockchain-Based Adaptive Security for Healthcare IoTs

Zulkifl, Zeeshan; Khan, Fawad; Tahir, Shahzaib; Afzal, Mehreen; Iqbal, Waseem; Rehman, Abdul; Saeed, Saqib; Almuhaideb, Abdullah M.

doi:10.1109/access.2022.3149046

Cited by 50 publications

(33 citation statements)

References 32 publications

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“…To ensure security, the fuzzy and block-based adaptive model was designed in [8]. Several uncertainties are still said to persist during the investment period and while acquiring the data for assessment.…”

Section: Related Workmentioning

confidence: 99%

Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-based IoT Sensor Communication for Remote Healthcare Analysis

Khalaf

Natarajan

Mahadev³

et al. 2024

IEEE Access

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In the remote healthcare industry data analytics denotes the computerization of collection, processing, and exploring complicated data to acquire finer perceptions and validate healthcare practitioners to produce familiar decisions. Healthcare basics in the modern age are vital challenges specifically in developing countries owing to the shortfall of difficult hospitals and medical professionals. As fuzzy systems have reformed several areas of work, health has also made the most of it. In this paper, the purpose of the study is to introduce a novel and intelligent remote healthcare system based on modern technologies like the Internet of things (IoT) and Neutrosophic fuzzy systems to ensure precise data analysis with lesser time and energy consumption. In this study, a novel method called, Blinder Oaxaca-based Shapiro Wilk Neutrosophic Fuzzy (BO-SWNF) data analytics for remote healthcare is designed. Data collection is performed with the WESAD dataset. Duplicated data are eliminated by Blinder Oaxaca Linear Regressionbased Preprocessing model. With the application of the Blinder Oaxaca function, energy efficiency is enhanced. Finally, the Shapiro Wilk Neutrosophic Fuzzy algorithm is applied for ensuring robust data analysis. The experimental results of the proposed BO-SWNF envisage the data for finer comprehension of attribute distribution. The result is conducted by using PYHTON application to analyze stress detection with the WESAD dataset. The proposed BO-SWNF method achieved an overall accurate data analysis of 12% with minimum time ensuring 56%improvement and minimizing energy consumption by 54%.

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Section: Related Workmentioning

confidence: 99%

Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-based IoT Sensor Communication for Remote Healthcare Analysis

Khalaf

Natarajan

Mahadev³

et al. 2024

IEEE Access

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“…Many researchers proposed different blockchain-based approaches to provide security to healthcare data and healthcare IoTs [ 81 , 82 ]. For example, Zeeshan Zulkifli et al [ 83 ] presented a new architecture that is based on fuzzy and blockchain enabling privacy and quick response facilities to the healthcare IoTs. By leveraging fuzzy logic, they suggest a behavior-driven adaptive security mechanism for healthcare IoTs and blockchain-based networks.…”

Section: Metaverse Healthcare Advantages and Challengesmentioning

confidence: 99%

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security

Ali

Abdullah

Armand

et al. 2023

Sensors

123

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Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

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“…The Authentication, Authorization, and Audit Logs (AAA) services were achieved by FBASHI, a system built on blockchain technology and fuzzy logic Zulkifl, Z. and Khan et al [ 37 ]. This work provided a heuristic method for conducting driven flexible security, offering AAA services for medical care IoTs and networks based on the blockchain.…”

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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FBASHI: Fuzzy and Blockchain-Based Adaptive Security for Healthcare IoTs

Cited by 50 publications

References 32 publications

Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-based IoT Sensor Communication for Remote Healthcare Analysis

Blinder Oaxaca and Wilk Neutrosophic Fuzzy Set-based IoT Sensor Communication for Remote Healthcare Analysis

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security

Managing Security of Healthcare Data for a Modern Healthcare System

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