Abstract
The detection of network fault scenarios was achieved using an appropriate subset of Management Information Base (MIB) variables. Anomalous changes in the behavior of the MIB variables was detected using a sequential Generalized Likelihood Ratio (GLR) test. This information was then temporally correlated using a duration filter to provide node level alarms which correlated with observed network faults and performance problems. The algorithm was implemented on data obtained from two different network nodes. The algorithm was optimized using five of the nine fault data sets, and it proved general enough to detect three of the remaining four faults. Consistent results were obtained from the second node as well. Detection of most faults occurred in advance (at least 5 minutes) of the fault suggesting the possibility of prediction and recovery in the future.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
