AUTHOR=Akram Faiza , Liu Dongsheng , Zhao Peibiao , Kryvinska Natalia , Abbas Sidra , Rizwan Muhammad TITLE=Trustworthy Intrusion Detection in E-Healthcare Systems JOURNAL=Frontiers in Public Health VOLUME=9 YEAR=2021 URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.788347 DOI=10.3389/fpubh.2021.788347 ISSN=2296-2565 ABSTRACT=

In Internet of Things (IoT)-based network systems (IoT-net), intrusion detection systems (IDS) play a significant role to maintain patient health records (PHR) in e-healthcare. IoT-net is a massive technology with security threats on the network layer, as it is considered the most common source for communication and data storage platforms. The security of data servers in all sectors (mainly healthcare) has become one of the most crucial challenges for researchers. This paper proposes an approach for effective intrusion detection in the e-healthcare environment to maintain PHR in a safe IoT-net using an adaptive neuro-fuzzy inference system (ANFIS). In the proposed security model, the experiments present a security tool that helps to detect malicious network traffic. The practical implementation of the ANFIS model on the MATLAB framework with testing and training results compares the accuracy rate from the previous research in security.