×

You are using an outdated browser Internet Explorer. It does not support some functions of the site.

Recommend that you install one of the following browsers: Firefox, Opera or Chrome.

Contacts:

+7 961 270-60-01
ivdon3@bk.ru

Network traffic monitoring using artificial intelligence methods for detect attacks

Abstract

Network traffic monitoring using artificial intelligence methods for detect attacks

Semykina N.A., Sadovnikova N.M.

Incoming article date: 14.03.2023

Nowadays, the organization security against cyber-attacks is a matter of great importance and a challenging area, as it affects them financially and functionally. Novel attacks are emerging daily, threatening a large number of businesses around the world. For this reason, the implementation and optimization of the performance of Intrusion Detection Systems is an urgent task. To solve this problem, the scientific community uses deep learning methods. In this paper, we pay special attention to attack detection methods built on different kinds of architectures, such as multilayer perceptron, gated recurrent unit, long short-term memory network, recurrent neural network, and convolutional neural network. To train and test their models, we used dataset UNSW-NB 15. The Australian Centre created this dataset for Cyber Security. It created to generate traffic, which is a hybrid of normal and attack activities. In finally we summarize this paper and discuss some ways to improve the performance of attack detection under thoughts of utilizing deep learning structures.Nowadays, the organization security against cyber-attacks is a matter of great importance and a challenging area, as it affects them financially and functionally. Novel attacks are emerging daily, threatening a large number of businesses around the world. For this reason, the implementation and optimization of the performance of Intrusion Detection Systems is an urgent task. To solve this problem, the scientific community uses deep learning methods. In this paper, we pay special attention to attack detection methods built on different kinds of architectures, such as multilayer perceptron, gated recurrent unit, long short-term memory network, recurrent neural network, and convolutional neural network. To train and test their models, we used dataset UNSW-NB 15. The Australian Centre created this dataset for Cyber Security. It created to generate traffic, which is a hybrid of normal and attack activities. In finally we summarize this paper and discuss some ways to improve the performance of attack detection under thoughts of utilizing deep learning structures.

Keywords: network traffic, computer attack, artificial neural network, traffic analysis, neural network configuration