Abstract
Virtualization technologies in cloud computing brings merits for resource utilization and on-demand sharing. However, users can face new security risks when they use the virtualized platforms. The co-resident attack means that the malicious users build side channels and threaten the virtual machines co-located on the same server. In 2017, Abazari et al. proposed a multi-objective, under the constraints of minimum cost and threat, response to co-resident attacks in cloud environment. In this paper, we aimed to propose a novel method for countermeasures decision to the attacks. We used machine learning to train the intrusion response model and conducted a set of experiments to demonstrate the effect of the proposed model. It showed that the near optimal solutions with good accuracy is obtainable and the response efficiency is improved.
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Acknowledgement
This research was partly supported by the Ministry of Science and Technology, Taiwan, under grant number MOST 107 - 2221 - E - 029 - 005 - MY3.
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Lin, CH., Lu, HW. (2020). Response to Co-resident Threats in Cloud Computing Using Machine Learning. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_76
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DOI: https://doi.org/10.1007/978-3-030-15032-7_76
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