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- W3090561185 abstract "The attractive characteristics of the cloud computing environment encourage its growth and penetration in various sections of society like government, education, entertainment, etc. The large-scale adoption of cloud computing not only provides services to users but also presents a wide attack landscape to the attackers and intruders in order to perform sophisticated attacks. Widespread implementation of cloud computing and its distributed and decentralized existence makes this computing paradigm prone to intrusion and attacks. Thus, the creation of network intrusion detection framework using anomaly detection method for cloud computing network with a better assault identification level and less false positives is essential. This paper discusses an effective network-based intrusion detection model utilizing artificial neural network strategies such as multilayer perceptron programmed with a genetic algorithm, as well as compares it using other machine learning techniques. The genetic algorithm was incorporated in multiple layer perceptron to predict the connection weights. Standard IDS dataset, namely CICIDS 2017, was used for simulation and testing of the suggested model. The results of implementation demonstrate the ability of the proposed model in the identification of intrusions in the cloud environment with a higher rate of detection and generation of minimal false alarm warnings, which suggests its dominance relative to state-of-the-art approaches. The implementation results show an accuracy of 90%." @default.
- W3090561185 created "2020-10-08" @default.
- W3090561185 creator A5036917415 @default.
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- W3090561185 date "2020-09-29" @default.
- W3090561185 modified "2023-09-23" @default.
- W3090561185 title "Multilayer Perceptron and Genetic Algorithm-Based Intrusion Detection Framework for Cloud Environment" @default.
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- W3090561185 doi "https://doi.org/10.1007/978-981-15-7130-5_38" @default.
- W3090561185 hasPublicationYear "2020" @default.
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