Abstract
In today’s digital world, information created by Internet of Things (IoT) devices has expanded drastically. This expansion is due to an increase in the number of IoT devices associated with the internet. Hybrid cloud computing provides enormous support to these emerging IoT devices in processing vast data. However, security is a challenging issue because of the integration of IoT and hybrid cloud. To achieve a sufficient level of hybrid cloud IoT security, a combination of Enhanced C4.5 machine learning algorithm and Dynamic Spatio Role-Based Access Control Algorithm is introduced. In this approach, the data users are classified using the Enhanced C4.5 algorithm and the user’s level of cloud data access is restricted using the Dynamic Spatio Role-Based Access Control Algorithm. As a result, the major security issues pertaining to IoT cloud are addressed. The security framework also uses a deduplication algorithm for eliminating redundant data and significantly improving IoT data storage requirements.
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Praveena, D., Thanga Ramya, S., Gladis Pushparathi, V.P., Bethi, P., Poopandian, S. (2021). Hybrid Cloud Data Protection Using Machine Learning Approach. In: Dash, S., Pani, S.K., Abraham, A., Liang, Y. (eds) Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing. Studies in Big Data, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-75657-4_7
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