2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 14 , Issue 2 , PP: 62-77, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Alaa Q. Raheema 1 *
Doi: https://doi.org/10.54216/JISIoT.140206
Deep learning offers practical answers for neural network models when applied to cloud registering security. Via robotization Distinguish dangers, decrease manual checking, and further develop in general security adequacy. Deep learning network models assume a pivotal part in security errands like interruption discovery, malware identification, anomaly recognition, and log examination. requires Deep Learning mix in cloud security cautiously assesses existing frameworks, characterizes goals, chooses dataset with arrangement, model tuning and last changes for consistence. Moreover, applying deep learning methods in cloud security requires thought of variables, for example, computational assets, information assortment, arrangement costs, model turn of events, mix endeavors, and continuous observing and support. This study proposes an artificial neural network (ANN) model portrayal in the cloud to track down cloud security parts and recreate security techniques and researches the essential moves toward coordinate these models in the cloud. Regarding that the adequacy of the ANN scheme relies upon cloud parameters like the nature of the preparation information and the network architecture Also, weight change calculations. The review emploies a dataset from Kaggle.com to approve the recreation and blueprints the means Partake in preparing and assessment of the ANN structure.
Cloud Computing , Artificial Neural Networks , Anomally Detection , Network Security , Deep Learning
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