A New Approach to Network Intrusion Detection Based on Gaussian Mixture Model

被引：0
|
作者：
He, Qian [1 ]
Zhang, Qian [1 ]
Wang, Lin [1 ]
Liang, Yi [1 ]
机构：
[1] Pattern Recognit & Intelligent Syst Lab Guizhou P, Guiyang 550025, Guzhou, Peoples R China
来源：
INTERNATIONAL CONFERENCE ON COMPUTATIONAL AND INFORMATION SCIENCES (ICCIS 2014) | 2014年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a novel approach for detecting network intrusions. A self-adaptive modeling formula based on Gaussian features is proposed firstly. Then the model is tested by simulating network traffic data. Experimental results obtained by applying this approach to five types of intrusions, including Dos, Probe, R2L, U2R and Guess_passwd, show that the proposed approach performs exceptionally with respective accuracy of 99.91%, 98.88%, 67.36%, 69.57% and 49.08%.
引用
收藏
页码：535 / 540
页数：6
相关论文
共 50 条
  • [1] An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset
    Zhang, Hongpo
    Huang, Lulu
    Wu, Chase Q.
    Li, Zhanbo
    [J]. COMPUTER NETWORKS, 2020, 177
  • [2] A Hybrid Intrusion Detection Algorithm Based on Gaussian Mixture Model and Nearest Neighbors
    Long, Chun
    Zhang, Yurou
    Wei, Jinxia
    Wan, Wei
    Zhao, Jing
    Du, Guanyao
    [J]. PROCEEDINGS OF THE IEEE LCN: 2019 44TH ANNUAL IEEE CONFERENCE ON LOCAL COMPUTER NETWORKS (LCN 2019), 2019, : 117 - 120
  • [3] A new intrusion detection approach based on network tomography
    National Computer Information Content Security Key Library, Harbin Institute of Technology, No. A3 Yumin Road, Beijing 100029, China
    [J]. WSEAS Trans. Inf. Sci. Appl., 2006, 2 (211-217):
  • [4] Anomaly Intrusion Detection System Using Gaussian Mixture Model
    Bahrololum, M.
    Khaleghi, A.
    [J]. THIRD 2008 INTERNATIONAL CONFERENCE ON CONVERGENCE AND HYBRID INFORMATION TECHNOLOGY, VOL 1, PROCEEDINGS, 2008, : 1162 - 1167
  • [5] An efficient approach for shadow detection based on Gaussian mixture model
    Han Yan-xiang
    Zhang Zhi-sheng
    Chen Fang
    Chen Kai
    [J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY, 2014, 21 (04) : 1385 - 1395
  • [6] An efficient approach for shadow detection based on Gaussian mixture model
    Yan-xiang Han
    Zhi-sheng Zhang
    Fang Chen
    Kai Chen
    [J]. Journal of Central South University, 2014, 21 : 1385 - 1395
  • [7] An efficient approach for shadow detection based on Gaussian mixture model
    韩延祥
    张志胜
    陈芳
    陈恺
    [J]. Journal of Central South University, 2014, 21 (04) : 1385 - 1395
  • [8] An intrusion detection method based on stacked sparse autoencoder and improved gaussian mixture model
    Zhang, Tianyue
    Chen, Wei
    Liu, Yuxiao
    Wu, Lifa
    [J]. COMPUTERS & SECURITY, 2023, 128
  • [9] ESVI-GaMM: A fast network intrusion detection approach based on the Bayesian gamma mixture model
    He, Wenda
    Cai, Xiangrui
    Lai, Yuping
    Yuan, Xiaojie
    [J]. INFORMATION SCIENCES, 2024, 678
  • [10] Network Intrusion Detection by Variational Component-Based Feature Saliency Gaussian Mixture Clustering
    Hong, Xin
    Papazachos, Zafeirios
    del Rincon, Jesus Martinez
    Miller, Paul
    [J]. COMPUTER SECURITY. ESORICS 2023 INTERNATIONAL WORKSHOPS, CPS4CIP, PT II, 2024, 14399 : 761 - 772
12345