Title :
Probabilistic network fault detection
Author :
Hood, Cynthia S. ; Ji, Chuanyi
Author_Institution :
Dept. of Comput. Sci., Illinois Inst. of Technol., Chicago, IL, USA
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;
Conference_Titel :
Global Telecommunications Conference, 1996. GLOBECOM '96. 'Communications: The Key to Global Prosperity
Print_ISBN :
0-7803-3336-5
DOI :
10.1109/GLOCOM.1996.591962
