Classification of types of data passing through the network using deep learning

Abstract

Network traffic classification is crucial for network security and management. Traditional methods often struggle with accuracy and scalability. This paper proposes a deep learning-based approach to classify various data types traversing a network. By leveraging the powerful feature extraction capabilities of deep neural networks, we aim to improve classification accuracy and adaptability to evolving network traffic patterns. We explore the application of convolutional neural networks (CNNs)  to capture both spatial and temporal dependencies within network packets. Experimental results demonstrate the effectiveness of our proposed method in accurately classifying different data types, surpassing traditional techniques in terms of precision, recall, and overall accuracy.

Our CNN model is designed to capture the underlying patterns and characteristics of network traffic. By processing raw traffic data as images, the model can learn to identify distinctive features that differentiate various traffic types.