Specific attack adjusted Bayesian network for intrusion detection system

被引:0
|
作者
Tuba, Milan [1 ]
Bulatovic, Dusan [1 ]
Miljkovic, Olga [1 ]
Simian, Dana [1 ]
机构
[1] Univ Belgrade, Fac Math, Studentski Trg 16, Belgrade 11001, Serbia
来源
MATHEMATICS AND COMPUTERS IN BIOLOGY AND CHEMISTRY | 2008年
关键词
privacy; security; networks; data protection; Bayesian network; intrusion detection system (IDS);
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper further examines Suitability of Bayesian networks for intrusion detection in computer networks. It is a continuation of [1]. Medical records should be readily available but also well protected, which are contrdictory goals. Automatic intrusion detection system (IDS) is required, but all used approaches have certain shortcomings. Bayesian networks are known to have good features, except that in general case are computationally too expensive. Here we show that limiting intrusion detection to any specific attack makes Bayesian networks manageable and suitable for the IDS. Example network is constructed and examined. Results are promising since with very limited computation and low sensitivity to the quality of prior knowledge, potentially dangerous Situations are successfully detected and classified. Such Bayesian network can represent an independent agent in a distributed system.
引用
收藏
页码:107 / +
页数:3
相关论文
共 50 条
  • [1] Network Intrusion Detection System based on Generative Adversarial Network for Attack Detection
    Das, Abhijit
    Balakrishnan, S. G.
    Pramod
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (11) : 757 - 766
  • [2] Intrusion Detection System Using Bayesian Network Modeling
    Alocious, Chaminda
    Abouzakhar, Nasser
    Xiao, Hannan
    Christianson, Bruce
    PROCEEDINGS OF THE 13TH EUROPEAN CONFERENCE ON CYBER WARFARE AND SECURITY (ECCWS-2014), 2014, : 223 - 232
  • [3] Using attack-specific feature subsets for network intrusion detection
    Shin, Sung Woo
    Lee, Chi Hoon
    AI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 4304 : 305 - +
  • [4] Attack classification research and a distributed network intrusion detection system
    Wang, X.-C.
    Liu, E.-D.
    Xie, X.-Q.
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2001, 38 (06): : 727 - 734
  • [5] A Neural Network Based System for Intrusion Detection and Attack Classification
    Subba, Basant
    Biswas, Santosh
    Karmakar, Sushanta
    2016 TWENTY SECOND NATIONAL CONFERENCE ON COMMUNICATION (NCC), 2016,
  • [6] NETWORK INTRUSION DETECTION SYSTEM USING ATTACK BEHAVIOR CLASSIFICATION
    Al-Jarrah, Omar
    Arafat, Ahmad
    2014 5TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), 2014,
  • [7] A framework of intrusion detection system based on Bayesian network in IoT
    Shi Q.
    Kang J.
    Wang R.
    Yi H.
    Lin Y.
    Wang J.
    Lin, Yun (linyun@hrbeu.edu.cn), 2018, Totem Publishers Ltd (14) : 2280 - 2288
  • [8] A Network Intrusion Detection Method Incorporating Bayesian Attack Graph and Incremental Learning Part
    Wu, Kongpei
    Qu, Huiqin
    Huang, Conggui
    FUTURE INTERNET, 2023, 15 (04):
  • [9] Network Intrusion Detection System for Jamming Attack in LoRaWAN join procedure
    Danish, Syed Muhammad
    Nasir, Arfa
    Qureshi, Hassaan Khaliq
    Ashfaq, Ayesha Binte
    Mumtaz, Shahid
    Rodriguez, Jonathan
    2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2018,
  • [10] Semi-Naive Bayesian Method for Network Intrusion Detection System
    Panda, Mrutyunjaya
    Patra, Manas Ranjan
    NEURAL INFORMATION PROCESSING, PT 1, PROCEEDINGS, 2009, 5863 : 614 - +