Abstract
The concept of industrial federated cloud computing has reduced the computational cost of an individual user. On the other side, it has increased the security and privacy issues of the information of a user. Users sharing computational resources in a federated cloud can be malicious and can gain access to the sensitive data of users of other cloud providers. Therefore, there is a need to monitor the behavior of users, exchanging data between different cloud servers. Using intrusion detection techniques, we can avoid the malicious traffic from gaining an access of the critical data of the users of other cloud providers. Moreover, by adding intrusion prevention technique, we can make industrial cyber physical systems more robust and efficient. With the increase of usage of cloud computing in the industry, the latest technological trends are moving towards the automation. This paper covers the detection and prevention of malicious activities in an environment of industrial cloud federation.
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Acknowledgments
We would like to acknowledge School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Pakistan and European Union (EU)’s Horizon 2020, Research and Innovation Staff Exchange Evaluations (RISE) under grant agreement no. 823904 – ENHANCE Project (MSCA-RISE 823904) for technical support and funding.
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Gerard, A., Latif, R., Iqbal, W., Gerard, N., Husnain Johar, A., Asghar, U. (2021). Detection and Prevention of a Malicious Activity in Industrial Federated Cloud Computing Paradigm. In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_52
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DOI: https://doi.org/10.1007/978-3-030-51041-1_52
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