An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment
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Title
An efficient metaheuristic algorithm based feature selection and recurrent neural network for DoS attack detection in cloud computing environment
Authors
Keywords
Cloud computing, DoS attack, Crow Search Algorithm, Opposition based learning, Recurrent neural network
Journal
APPLIED SOFT COMPUTING
Volume 100, Issue -, Pages 106997
Publisher
Elsevier BV
Online
2020-12-13
DOI
10.1016/j.asoc.2020.106997
References
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- A note on “Opposition versus randomness in soft computing techniques” [Appl. Soft Comput. 8 (2) (2008) 906–918]
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