A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier

• المؤلفون الرئيسيون: Azad, Saiful, Amin Salem, Saleh Bllagdham, Mahmud, Mufti, Kaiser, M. Shamim, Miah, Md Saef Ullah
• التنسيق: Conference or Workshop Item
• اللغة: English; English
• منشور في: Institute of Electrical and Electronics Engineers Inc. 2021
• الموضوعات: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• الوصول للمادة أونلاين: http://umpir.ump.edu.my/id/eprint/42379/1/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare.pdf; http://umpir.ump.edu.my/id/eprint/42379/2/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare%20things%20using%20an%20improved%20fp-growth%20algorithm%20and%20na%C3%AFve%20bayes%20classifier_ABS.pdf; http://umpir.ump.edu.my/id/eprint/42379/; https://doi.org/10.1109/STI53101.2021.9732588

Healthcare 4.0 has revolutionized the delivery of healthcare services during the last years. Facilitated by it, many hospitals have migrated to the paradigm of being smart. Smartization of hospitals has reduced healthcare costs while providing improved and reliable healthcare services. Thanks to the Internet of Healthcare Things (IoHT) based healthcare delivery frameworks, integration of many heterogeneous devices with varying computational capabilities has been possible. However, this introduced a number of security concerns as many secure communication protocols for traditional networks can not be verbatim employed on these frameworks. To ensure security, the threats can largely be tackled by employing a Trust Management Model (TMM) which will critically evaluate the behavior or activity pattern of the nodes and block the untrusted ones. Towards securing these frameworks through an intelligent TMM, this work proposes a machine learning based Behavioral Trust Model (BTM), where an improved Frequent Pattern Growth (iFP-Growth) algorithm is proposed and applied to extract behavioral signatures of various trust classes. Later, these behavioral signatures are utilized in classifying incoming communication requests to either trustworthy and untrustworthy (trust) class using the Naïve Bayes classifier. The proposed model is tested on a benchmark dataset along with other similar existing models, where the proposed BMT outperforms the existing TMMs.