New automatic (IDS) in IoTs with artificial intelligence technique

Abstract

In this study, we will be developed new framework-based deep learning techniques for IDS detection in a wireless sensor network. This study uses three methods for developing Network Intrusion Detection systems in IoTs. In the first method, we try to train the deep learning technique using an optimization algorithm. The aim of using optimization algorithm BBO and other algorithms to develop IoTs security system. In the second method, an optimization algorithm is applied as a feature selection method and combined with a classifier to develop a new Network Intrusion Detection system. Finally, CNN Convolution Neural Network LSTM (Long Short-Term Memory layer) was applied with an "Adam "optimizer to train and evaluate data and to read and drop any invalid data from the dataset.