  Intrusion Detection is an invaluable part of computer networks defense. An
important consideration is the fact that raising false alarms carries a
significantly lower cost than not detecting at- tacks. For this reason, we
examine how cost-sensitive classification methods can be used in Intrusion
Detection systems. The performance of the approach is evaluated under different
experimental conditions, cost matrices and different classification models, in
terms of expected cost, as well as detection and false alarm rates. We find
that even under unfavourable conditions, cost-sensitive classification can
improve performance significantly, if only slightly.
