Learning patterns of behavior by observing system events

Abstract

The proposed algorithm (BPL) induces behavior patterns from events taking into account characteristics of observed systems and their environment. The main strategy of this method consists on building summaries of the behaviour of a system as events arrive, and take these summaries as training examples. BPL constructs summaries with new features from events, like duration of current event values, repetitions of an event in a period of time, amongst others. This algorithm has been tested in learning faulty behavior of networks with the purpose of continuously predicting alarms.