Fuzzy-Enhanced Secure Messaging Framework for Smart Healthcare System

【Author】 Patel, Nishi; Patel, Dhyan; Jadav, Nilesh Kumar; Rathod, Tejal; Tanwar, Sudeep; Pau, Giovanni; Sharma, Gulshan; Alqahtani, Fayez; Tolba, Amr

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】The healthcare industry is exponentially growing its dependence on smart wearables and remote devices for efficient treatment and diagnosis. These smart devices benefit the healthcare industry, but they raise serious security and integrity concerns while exchanging healthcare data. These devices are primarily meant for data dissemination; hence, they are equipped with weak security protocols that are susceptible to attacks like distributed denial-of-service (DDoS), data injection, and man-in-the-middle (MiTM) attacks. To circumvent the aforementioned security challenges, this article proposed a secure and intelligent data exchange framework for smart healthcare systems. For that, we amalgamate artificial intelligence (AI) and blockchain technology to strengthen the security of data dissemination between smart medical devices. Further, we adopted fuzzy logic that extracts the essential features from the healthcare security dataset to enhance the detection rate of AI models. We used different AI algorithms such as logistic regression (LR), random forest (RF), decision trees (DT), stochastic gradient descent (SGD), and Gaussian naive Bayes (GNB) to classify healthcare data into malicious and non-malicious. The predicted data can still be maneuvered by adversaries that introduce subtle changes that skew the results to their advantage. Therefore, we employed blockchain technology that stores non-malicious healthcare data (predicted data) from data tampering attacks. The developed smart contract validates the non-malicious healthcare data and only allows them to be securely stored inside the interplanetary file system (IPFS)-based public blockchain. The proposed framework is evaluated by considering various evaluation metrics like recall, precision, accuracy, F1 score, area under the curve (AUC) score, and blockchain scalability.

【Keywords】Medical services; Artificial intelligence; Blockchains; Accuracy; Feature extraction; Sensors; Medical information systems; Data security; Machine learning; Wearable devices; Smart contracts; Healthcare data security; blockchain; fuzzy logic; artificial intelligence; machine learning; wearable devices; smart contract

【发表时间】2024

【收录时间】2024-08-10

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-医疗领域