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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 1, 2019.
Abstract: IoT-based systems have considerable dynamic behavior and heterogeneous technology participants. The corresponding threats and security operations are also complex to handle. Traditional security solutions may not be appropriate and effective in such ecosystems as they recognize and assess a limited context, they work well only with high-end and specific computing platforms, and implement manual response mechanisms. We have identified the security objectives of a potential IoT-eHealth system and have proposed a security model that can efficiently achieve them. The proposed model is a context-aware and self-adaptive security management model for IoT, in eHealth perspective that will monitor, analyze, and respond to a multitude of security contexts autonomously. As these operations are planned at the gateway level, the model exploits the advantages of computing in the Fog Layer. Moreover, the proposed model offers flexibility and open connectivity to allow any smart device or thing to be managed irrespective of their native design. We have also explained how our model can establish and serve the essential security objectives of an IoT-based environment.
Waqas Aman and Firdous Kausar, “Towards a Gateway-based Context-Aware and Self-Adaptive Security Management Model for IoT-based eHealth Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 10(1), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100137
@article{Aman2019,
title = {Towards a Gateway-based Context-Aware and Self-Adaptive Security Management Model for IoT-based eHealth Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100137},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100137},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {1},
author = {Waqas Aman and Firdous Kausar}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.