ANNIDS: Intrusion detection system based on artificial neural network

被引：7
|
作者：
Liu, YH [1 ]
Tian, DX [1 ]
Wang, AM [1 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Jilin, Peoples R China
来源：
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词：
intrusion detection system; neural network; packet; hamming distance;
D O I：
10.1109/ICMLC.2003.1259699
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper describes a network intrusion detection system based on artificial neural network (ANNIDS). The advantage of neural network ensures that ANNIDS does not need expert knowledge and it can find unknown or novel intrusions. The key part of ANNIDS is an adaptive resonance theory neural network (ART). ANNIDS can be trained in real-time and in an unsupervised way. A weight hamming distance method is used in detection, which is simple and correct in finding anomalous behavior. A well-trained ANNIDS can monitor the network in real time. The experimental results show that ANNIDS performs best when vigilance parameter is 0.4 to 0.5 and intrusion threshold is 0.4. The false positive error is about 8%, the negative error is about 2%, and the total error is lower 10%.
引用
收藏
页码：1337 / 1342
页数：6
相关论文
共 50 条
  • [1] Hacker Intrusion Detection System based on Artificial Neural Network
    Huang, Jing
    Chen, Hai Bin
    Zhang, Jiang
    Zhang, Han Bo
    [J]. INFORMATION TECHNOLOGY APPLICATIONS IN INDUSTRY, PTS 1-4, 2013, 263-266 : 2924 - +
  • [2] Research and Implementation of Intrusion Detection System Based on Artificial Neural Network
    Han Xiaocui
    [J]. MATERIALS AND MANUFACTURING TECHNOLOGY, PTS 1 AND 2, 2010, 129-131 : 1421 - 1425
  • [3] The research and implementation of intelligent intrusion detection system based on artificial neural network
    Li, J
    Zhang, GY
    Gu, GC
    [J]. PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 3178 - 3182
  • [4] An artificial-neural-network-based multiple classifiers intrusion detection system
    Deng, Hao-Ran
    Wang, Yun-Hong
    [J]. 2007 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION, VOLS 1-4, PROCEEDINGS, 2007, : 683 - 686
  • [5] Artificial Neural Network Classifier for Intrusion Detection System in Computer Network
    Lokeswari, N.
    Rao, B. Chakradhar
    [J]. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION TECHNOLOGIES, IC3T 2015, VOL 3, 2016, 381 : 581 - 591
  • [6] An Intrusion Detection System Based On Neural Network
    Can, Okan
    Sahingoz, Ozgur Koray
    [J]. 2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2015, : 2302 - 2305
  • [7] Improving Intrusion Detection System using Artificial Neural Network
    Albahar, Marwan Ali
    Binsawad, Muhammad
    Almalki, Jameel
    El-Etriby, Sherif
    Karali, Sami
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (06) : 578 - 588
  • [8] Privacy Protection and Intrusion Detection System of Wireless Sensor Network Based on Artificial Neural Network
    Shi, Lusheng
    Li, Kai
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [9] Intrusion detection approach based on optimised artificial neural network
    Choras, Michal
    Pawlicki, Marek
    [J]. NEUROCOMPUTING, 2021, 452 : 705 - 715
  • [10] Applying Artificial Neural Network and eXtended Classifier System for Network Intrusion Detection
    Alsharafat, Wafa'
    [J]. INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2013, 10 (03) : 230 - 238
12345