Probabilistic network fault detection

Author

Hood, Cynthia S. ; Ji, Chuanyi

Author_Institution

Dept. of Comput. Sci., Illinois Inst. of Technol., Chicago, IL, USA

Volume

3

fYear

1996

Firstpage

1872

Abstract

To improve network management in today´s high-speed communication networks, we propose an intelligent system using adaptive learning machines. The system learns the normal behavior of the network. Deviations from the norm are detected and the information is combined in the probabilistic framework of a Bayesian network. The proposed system is thereby able to detect unknown or unseen faults. As demonstrated on real network data, this method can detect abnormal behavior before a fault actually occurs, giving the network management system (human or automated) the ability to avoid a potentially serious problem

Keywords

Bayes methods; adaptive systems; fault diagnosis; learning systems; probability; telecommunication computing; telecommunication network management; Bayesian network; abnormal behavior detection; adaptive learning machines; high-speed communication networks; intelligent system; network management system; probabilistic network fault detection; real network data; Adaptive systems; Application software; Bayesian methods; Communication networks; Computer network management; Fault detection; Fault diagnosis; Hardware; Learning systems; Machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Global Telecommunications Conference, 1996. GLOBECOM '96. 'Communications: The Key to Global Prosperity

Print_ISBN

0-7803-3336-5

Type

conf

DOI

10.1109/GLOCOM.1996.591962

Filename

591962