From the last decade, the use of internet and its growth is continuously increasing. Similarly, numbers of services are coming out along with the internet

Because of the ever increasing number of Internet users, Internet security is becoming more essential. To identify and detect attackers, many researchers utilized data mining methods. Existing data mining techniques are unable to provide a sufficient degree of detection precision. An intrusion detection system for wireless networks is being developed to ensure data transmission security. The Network Intrusion Detection Algorithm (NIDS) uses a deep classification system to classify network connections as good or harmful. Deep Convolution Neural Network (DCNN), Deep Recurrent Neural Network (DRNN), Deep Long Short-Term Memory (DLSTM), Deep Convolution Neural Network Long Short-Term Memory (DCNN LSTM), and Deep Gated Recurrent Unit (DGRU) methods that use NSLKDD data records to train models are proposed. The experiments were carried out for a total of 1000 epochs. During the experiment, we achieved a model accuracy of more than 98 percent. We also discovered that as the number of layers in a model grows, so does the accuracy.