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- W4289653657 abstract "Intensive parallel applications are being executed by using cloud computing techniques. In cloud computing maintaining security of user’s data base is the biggest issue. Due to this issue several factors like confidentiality, performance, integrity, efficiency affects a lot. Here in this paper an obtrusion detection model is proposed which is used to identify various types of threats of detecting obtrusion present in the system by using cloud computing techniques. Here private and hybrid cloud model is used to detect obtrusion. The private cloud obtrusion detection model uses policy-based authentication to offer security to user’s database while the Hybrid cloud obtrusion detection model used to limit the traffic occurred during packet transmission. As a name suggest it consists of recurrent neural network (RNN) along with Normalized K-mean algorithm. With the help of these proposed model following benefits like less uploading time and retrieving time, low storage space, less normalization cost, less computational cost is achieved. To access these models cloud users need to authenticate by policy-based authentication after that key managers allocate public keys to the licensed cloud users." @default.
- W4289653657 created "2022-08-03" @default.
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- W4289653657 date "2022-08-04" @default.
- W4289653657 modified "2023-09-28" @default.
- W4289653657 title "Obtrusion Detection Using Hybrid Cloud Models" @default.
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- W4289653657 doi "https://doi.org/10.1007/978-981-19-2065-3_14" @default.
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