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
相关论文
共 50 条
  • [11] Intrusion Detection using Deep Belief Networks
    Alom, Md. Zahangir
    Bontupalli, VenkataRamesh
    Taha, Tarek M.
    PROCEEDINGS OF THE 2015 IEEE NATIONAL AEROSPACE AND ELECTRONICS CONFERENCE (NAECON), 2015, : 339 - 344
  • [12] USING NEURAL NETWORKS IN INTRUSION DETECTION SYSTEMS
    Merhaut, Filip
    Zelinka, Ivan
    MENDEL 2008, 2008, : 172 - 174
  • [13] Intrusion detection using hierarchical neural networks
    Zhang, CL
    Jiang, J
    Kamel, M
    PATTERN RECOGNITION LETTERS, 2005, 26 (06) : 779 - 791
  • [14] Intrusion Detection Using Evolutionary Neural Networks
    Michailidis, Emmanuel
    Katsikas, Sokratis K.
    Georgopoulos, Efstratios
    PCI 2008: 12TH PAN-HELLENIC CONFERENCE ON INFORMATICS, PROCEEDINGS, 2008, : 8 - +
  • [15] Intrusion detection using PCASOM neural networks
    Liu, Guisong
    Yi, Zhang
    ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS, 2006, 3973 : 240 - 245
  • [16] On learning effective ensembles of deep neural networks for intrusion detection
    Folino, F.
    Folino, G.
    Guarascio, M.
    Pisani, F. S.
    Pontieri, L.
    INFORMATION FUSION, 2021, 72 : 48 - 69
  • [17] Accelerated Deep Neural Networks for Enhanced Intrusion Detection System
    Potluri, Sasanka
    Diedrich, Christian
    2016 IEEE 21ST INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2016,
  • [18] Comparison of Shallow and Deep Neural Networks for Network Intrusion Detection
    Kim, Daniel E.
    Gofman, Mikhail
    2018 IEEE 8TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2018, : 204 - 208
  • [19] Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks
    Ibitoye, Olakunle
    Shafiq, Omair
    Matrawy, Ashraf
    2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,
  • [20] Method of Intrusion Detection using Deep Neural Network
    Kim, Jin
    Shin, Nara
    Jo, Seung Yeon
    Kim, Sang Hyun
    2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP), 2017, : 313 - 316
12345