A review on Deep Neural Network for Computer Network Traffic Classification

TL;DR

This paper reviews various deep neural network architectures used for classifying computer network traffic, including malicious and normal activities, highlighting their accuracy and differences.

Contribution

It provides a comparative analysis of existing neural network models for network traffic classification, emphasizing their effectiveness in intrusion detection.

Findings

Different NN architectures vary in accuracy for traffic classification

Convolutional Recurrent Neural Networks show promising results

The review highlights strengths and limitations of each model

Abstract

Focus on Deep Neural Network based malicious and normal computer Network Traffic classification. (such as attacks, phishing, any other illegal activity and normal traffic identification). In this paper, the main idea is to review, existed Neural Network based network traffic classification. Which indicates intrusion activity classification and detection. It is very important to classify network traffic to safeguard any system, connected to computer network. There are a variety of NN architecture for it, with different rate of accuracy. On this paper we will do relative compression among them. Index Terms-Computer Network, Network traffic, Packet, Intrusion, DOS (Denial-of-service), unauthorized access, IDS (Intrusion Detection System), IPS (Intrusion Prevention Systems), R2L (Remote to Local Attack), Probing, U2R (User to Root Attack), DNN (Deep Neural Network), CRNN (Convolutional Recurrent Neural Network), RPROP (Resilient propagation).