Network intrusion detection systems (NIDS) are actually used to detect suspicious activities such as viruses, shellcode, XSS, CSRF, worms, etc. There are two types of the NIDS: signature-based and anomaly-based. Recently, Deep Learning have emerged as promising techniques for classifying network attacks. In this paper, we propose a method to analyze the network traffic behavior through Deep Learning classification techniques using traffic features. The results indicate that Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) techniques achieved similar performance with 94% accuracy when using all features in the used dataset. However, with the use of feature selection techniques such as XGBoost, Pearson correlation, and mutual information, the models achieved a slightly lower accuracy of 91%, but these results demonstrate the effectiveness of feature selection methods in enhancing the performance of Deep Learning models by reducing complexity and removing irrelevant features.