Cyberattacks Detection Through Behavior Analysis of Internet Traffic

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.