Recurrent Neural Networks and Deep Neural Networks Based on Intrusion Detection System

The computer security has become a major challenge. Tools and mechanisms have been developed to ensure a level of compliance. These include the Intrusion De-tection Systems (IDS). The principle of conventional IDS is to detect attempts to attack a network and to identify abnormal activities and behaviors. The reasons, including the uncertainty in searching for types of attacks and the increasing com-plexity of advanced cyber-attacks, IDS calls for the need for integration of meth-ods such as Deep Neuron Networks (DNN) and Recurring Neuron Networks (RNN) more precisely long-term memory (LSTM). In this submission, DNN and LSTM were used to predict attacks against the Network Intrusion Detection Sys-tem (NIDS). In this memory, we used four hidden layers for all deep learning algo-rithms, forty-one layers of inputs and two layers of outputs and with 100 itera-tions. In fact, learning is kept constant at 0.01 while the other parameters are optimized. After that for DNN, the number of neurons of the first hidden layer was further increased to 1280 but did not give any appreciable increase in accuracy. Therefore, the number of neurons has been set to 1024 and the LSTM we set the number of neurons of all hidden layers to 32. The results were compared and con-cluded that a three-layer LSTM performs better than all other conventional ma-chine learning and deep learning algorithms.