Some aspects of neural network approach for intrusion detection

被引:0
|
作者
Golovko, V [1 ]
Kochurko, P [1 ]
机构
[1] Brest State Tech Univ, Brest 224017, BELARUS
来源
CYBERSPACE SECURITY AND DEFENSE: RESEARCH ISSUES | 2005年 / 196卷
关键词
neural networks; intrusion detection systems; PCA; network attacks; attack recognition and identification;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Intrusion detection techniques are of great importance for computer network protecting because of increasing the number of remote attack using TCP/IP protocols. There exist a number of intrusion detection systems, which are based on different approaches for anomalous behavior detection. This paper focuses on applying neural networks for intrusion detection and recognition. It is based on nonlinear PCA network and multilayer perceptron. The 1999 KDD Cup data set is used for F training and testing neural networks. The results of experiments are discussed in the paper.
引用
收藏
页码:367 / 382
页数:16
相关论文
共 50 条
  • [21] Neural Network Based Intrusion Detection Systems with Different Training Functions
    Karatas, Gozde
    Sahingoz, Ozgur Koray
    2018 6TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSIC AND SECURITY (ISDFS), 2018, : 73 - 78
  • [22] Low-Latency Intrusion Detection Using a Deep Neural Network
    Bin Ahmad, Umair
    Akram, Muhammad Arslan
    Mian, Adnan Noor
    IT PROFESSIONAL, 2022, 24 (03) : 67 - 72
  • [23] Statistical Metamorphic Testing of Neural Network Based Intrusion Detection Systems
    Reitman, Faqeer Ur
    Izurieta, Clemente
    PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE (IEEE CSR), 2021, : 20 - 26
  • [24] The Use of Artificial Neural Networks in Network Intrusion Detection: A Systematic Review
    Oney, Mehmet Ugur
    Peker, Serhat
    2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP), 2018,
  • [25] Network Intrusion Detection Based on Extended RBF Neural Network With Offline Reinforcement Learning
    Lopez-Martin, Manuel
    Sanchez-Esguevillas, Antonio
    Ignacio Arribas, Juan
    Carro, Belen
    IEEE ACCESS, 2021, 9 : 153153 - 153170
  • [26] Stepping-Stone Intrusion Detection Using Neural Networks Approach
    Wu, Han-Ching
    Huang, Shou-Hsuan Stephen
    NOVEL ALGORITHMS AND TECHNIQUES IN TELECOMMUNICATIONS, AUTOMATION AND INDUSTRIAL ELECTRONICS, 2008, : 358 - 363
  • [27] Multisensor neural network approach to mine detection
    Iler, AL
    Marble, JA
    Rauss, P
    DETECTION AND REMEDIATION TECHNOLOGIES FOR MINES AND MINELIKE TARGETS VI, PTS 1 AND 2, 2001, 4394 : 1003 - 1010
  • [28] A machine learning approach for improving the performance of network intrusion detection systems
    Azizan A.H.
    Mostafa S.A.
    Mustapha A.
    Mohd Foozy C.F.
    Abd Wahab M.H.
    Mohammed M.A.
    Khalaf B.A.
    Annals of Emerging Technologies in Computing, 2021, 5 (Special issue 5) : 201 - 208
  • [29] Intrusion Detection System Based on Fast Hierarchical Deep Convolutional Neural Network
    Mendonca, Robson V.
    Teodoro, Arthur A. M.
    Rosa, Renata L.
    Saadi, Muhammad
    Melgarejo, Dick Carrillo
    Nardelli, Pedro H. J.
    Rodriguez, Demostenes Z.
    IEEE ACCESS, 2021, 9 : 61024 - 61034
  • [30] Feature selection and deep learning approach for anomaly network intrusion detection
    Bennaceur, Khadidja
    Sahraoui, Zakaria
    Nacer, Mohamed Ahmad
    INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY, 2024, 23 (04) : 433 - 453