Deep Learning for Network Traffic Monitoring and Analysis

Abstract Book of the 10th International Conference on Advanced Research in Management, Business and Finance

Year: 2026

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Deep Learning for Network Traffic Monitoring and Analysis

Motasem AbuDawas

ABSTRACT:

Network activity monitoring and analysis are very important in maintaining computer network security as well as optimizing their performance. However, the traditional means have faced difficulties in keeping up with the ever-increasing volume, dynamic nature, and complexity of network traffic. These limitations necessitated the adoption of new approaches that address the changing characteristics of network traffic data. In this regard, researchers have made good use of deep learning techniques to overcome such challenges. This paper offers a broad survey on how deep learning is used for monitoring and analyzing network traffic. We therefore describe some constraints inherent in traditional methods and the benefits brought about by using deep learning systems. It also includes an overview of typical neural network architectures for deep learning like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Moreover, this study touches on other uses of deep learning in this field such as intrusion detection, anomaly detection, traffic classification as well as predictive analytics among others. Similarly, it discusses how experiments were done to realize if there was any practical application according to this study or not. Lastly, the current state of the art would be assessed using experimental evaluations and real-world case studies to illustrate the effectiveness of several deep learning techniques adopted towards these ends. Finally, we highlight some current problem areas in this field and suggest potential future research directions for furthering our understanding of the applicability of deep learning to network traffic monitoring and analysis

Keywords: Deep learning, Network traffic monitoring, Network traffic analysis, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Intrusion detection, Anomaly detection, Traffic classification, Predictive analytics