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Safety is a critical concern in the field of information systems, because of the enormous amount
of internet traffic. The way individuals live, work, and study is changing due to the increasing use of the
Internet, which is causing more and more serious security threats in everyday life. This paper discusses how
to model a deep learning-based intrusion detection system and proposes a deep learning approach to
intrusion detection using recurrent neural networks (RNN-IDS). here analyze the model's performance in
binary classification and multiclass classification, and the number of neurons and different learning rate
impacts on the model's output. They equate it with J48's, artificial neural network, random forest, help vector
machine, and other machine learning approaches proposed on the benchmark data set by past researchers.
The RNN-IDS model increases intrusion detection performance and provides a new method of examining
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