Learning Patterns of Behavior by Observing System Events

Authors:
Marlon Núñez
Affiliations:
-
Venue:
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Year:
2000

Citing 2
Cited 1

The Use of Background Knowledge in Decision Tree Induction

Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology

Features for learning local patterns in time-stamped data

LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection

Quantified Score

Hi-index	0.00

Visualization

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.