In recent years, there has been a perceptible rise in the number of attacks that involve breaking into computer networks, which is cause for big factor from both a privacy and a security standpoint. The proliferation of new technologies has led to an increase in the sophistication of cyber-security breaches, to the point that the currently available monitoring tools are unable to adequately handle the problem. In consideration of this, the installation of a network intrusion detection system that is both intelligent and efficient would be absolutely necessary in order to resolve this issue. In this paper, we proposed a model using Recursive Feature Elimination and Ensemble Learning. Features are extracted using Recursive Feature Elimination and RNN. Furthermore, we compare the outcomes of our proposed solution with those of other suggested policies in an effort to identify the method that delivers the most appropriate method for the intrusion detection systems, and we assess the effectiveness of the suggested solution using a number of evaluation matrices.
Key words: Recurrent Neural Network, Intrusion Detection, Recursive Feature Elimination.
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