Detection and Analysis of Intrusion Attacks Using Deep Neural Networks

被引：2
|
作者：
Takeda, Atsushi [1 ]
机构：
[1] Tohoku Gakuin Univ, Izumi Ku, 2-1-1 Tenjinzawa, Sendai, Miyagi 9813193, Japan
来源：
ADVANCES IN NETWORK-BASED INFORMATION SYSTEMS, NBIS-2022 | 2022年 / 526卷
关键词：
D O I：
10.1007/978-3-031-14314-4_26
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：258 / 266
页数：9
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