• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/
Menu

Network based Anomaly Intrusion Detection using Bayesian Network Techniques


Cha ByungRae, Park KyoungWoo, Seo JaeHyun, Journal of Internet Computing and Services, Vol. 6, No. 1, pp. 27-38, Feb. 2005
Full Text:  HTML
Keywords: Network Anomaly Intrusion Detection, Bayesian Network

Abstract

Recently, the rapidly development of computing environments and the spread of Internet make possible to obtain and use of information easily. Immediately, by opposition function the Hacker's unlawful intrusion and threats rise for network environments as time goes on. Specially, the internet consists of Unix and TCP/IP had many vulnerability. the security techniques of authentication and access controls cannot adequate to solve security problem, thus IDS developed with 2nd defence line. In this paper, intrusion detection method using Bayesian Networks estimated probability values of behavior contexts based on Bayes theory. The contexts of behaviors or events represents Bayesian Networks of graphic types. We profiled concisely normal behaviors using behavior context. And this method be able to detect new intrusions or modificated intrusions. We had simulation using DARPA 2000 Intrusion Data.


Statistics
Show / Hide Statistics

Statistics (Past 3 Years)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[APA Style]
ByungRae, C., KyoungWoo, P., & JaeHyun, S. (2005). Network based Anomaly Intrusion Detection using Bayesian Network Techniques. Journal of Internet Computing and Services, 6(1), 27-38.

[IEEE Style]
C. ByungRae, P. KyoungWoo, S. JaeHyun, "Network based Anomaly Intrusion Detection using Bayesian Network Techniques," Journal of Internet Computing and Services, vol. 6, no. 1, pp. 27-38, 2005.

[ACM Style]
Cha ByungRae, Park KyoungWoo, and Seo JaeHyun. 2005. Network based Anomaly Intrusion Detection using Bayesian Network Techniques. Journal of Internet Computing and Services, 6, 1, (2005), 27-38.