Detection and Analysis of Intrusion Attacks Using Deep Neural Networks
Detection and Analysis of Intrusion Attacks Using Deep Neural Networks
复制标题
使用深度神经网络检测和分析入侵攻击
DOI：
10.1007/978-3-031-14314-4_26
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Takeda Atsushi
中科院分区：
文献类型：
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作者：
Jun Isshiki;Ryo Sugai and Akira Saito;Takeda Atsushi
由于对服务器的入侵攻击数量不断增加，入侵检测系统变得越来越必要。攻击者试图以各种方式入侵服务器。因此，人工制定检测规则比较困难，因此需要基于机器学习的入侵检测系统。提出了一种用于入侵检测系统的深度神经网络。此外，本文还给出了实验结果，表明了该神经网络的性能。本文的实验结果也说明了检测入侵攻击并不容易的原因。
Intrusion detection systems are becoming more necessary because the number of intrusion attacks on servers is increasing. Attackers try to intrude on the servers in various ways. Therefore, intrusion detection systems based on machine learning are required because it is hard to make the detection rules manually. This paper presents a deep neural network for intrusion detection systems. In addition, this paper shows experimental results which indicate the performance of the proposed neural network. The experimental results in this paper also indicate why detecting intrusion attacks is not easy.