IRNN-GDX: An improved random neural network using GDX for Intrusion Detection Systems

摘要

Anintrusion detection system is one of the methods that help when intrusion is detected to determine scheme safety. In this paper, NSL-KDD data set efficiency is assessed using enhanced RNN with momentum gradient descent and adaptive learning rate (GDX). Before destabilizing the core network, an intelligent IDS should be used to achieve this goal, and we propose a new RNN-GDX algorithm.Based on multiple performance measures, results are analyzed and better accuracy has been discovered. We achieved minimum (MSE) mean square error rate and maximum accuracy Using the RNN-GDX algorithm for NSL-KDD data set results showed that RNN-GDX learned better as well as overall efficiency isincreased to 93.3 percent & 97.68 percent respectively, with 29 input and 29 hidden layer neurons and 41 input & 41 hidden layer neurons changing to the lowest value of 0.01.