Detection and Discrimination of Injected Network Faults
Detection and Discrimination of Injected Network Faults
by Roy A. Maxion and Robert T. Olszewski
Abstract
Six hundred faults were induced by injection into five live campus
networks at Carnegie Mellon University in order to determine whether or
not particular network faults have unique signatures as determined by
out-of-band monitoring instrumentation. If unique signatures span
networks, then the monitoring instrumentation can be used to diagnose
network faults, or distinguish among fault classes, without human
intervention, using machine-generated diagnostic decision rules. This
would be especially useful in large, unmanned systems in which the
occurrence of novel or unanticipated faults could be catastrophic.
Results indicate that significant accuracy in automated detection and
discrimination among fault types can be obtained using anomaly
signatures as described here.
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Last Modified: Wed Mar 14 15:40:34 EST 2001