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المؤلفAwan, Kamran Ahmad
المؤلفDin, Ikram Ud
المؤلفAlmogren, Ahmad
المؤلفAlmajed, Hisham
المؤلفMohiuddin, Irfan
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-10-27T09:31:33Z
تاريخ النشر2021-11-01
اسم المنشورIEEE Internet of Things Journal
المعرّفhttp://dx.doi.org/10.1109/JIOT.2020.3029221
الاقتباسAwan, K. A., Din, I. U., Almogren, A., Almajed, H., Mohiuddin, I., & Guizani, M. (2020). NeuroTrust—Artificial-Neural-Network-Based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things. IEEE Internet of Things Journal, 8(21), 15672-15682.‏
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85118380937&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/35527
الملخصInternet of Medical Things (IoMT) provides a diverse platform for healthcare to enhance the accuracy, reliability, and efficiency. In addition, it utilizes the productivity of available equipment to improve patients' health. IoMT also provides distinct ways by which healthcare will be revolutionized as it provides numerous opportunities to handle operations with precision. However, numerous advantages have raised several security challenges, such as trust, data integrity, network constraints, and real-time processing among others. There is a requirement for a robust approach to maintain data integrity along with the behavior detection of nodes to completely maintain a secure environment. In the proposed approach, the mechanism is capable of maintaining a robust network by predicting and eliminating malicious nodes. The proposed NeuroTrust approach utilizes the trust parameters to evaluate the degree of trust that include reliability, compatibility, and packet delivery. This approach also lightens the two-way computation burden and uses a lightweight encryption mechanism to further enhance the security and integrity during data dissemination, which is required for the digital revolution in delivering efficient high quality healthcare. The performance of the proposed approach has been extensively evaluated against the absolute trust formulation, accuracy of trust computation, energy consumption, and several potential attacks. The simulation results show the effective performance to identify malicious and compromised nodes, and maintain resilience against various attacks.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعArtificial neural network
efficient healthcare
integrity
Internet of Medical Things (IoMT)
trust management
العنوانNeuroTrust - Artificial-Neural-Network-Based Intelligent Trust Management Mechanism for Large-Scale Internet of Medical Things
النوعArticle
الصفحات15672-15682
رقم العدد21
رقم المجلد8
dc.accessType Abstract Only


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