Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc NETworks (MANETs). In this paper, the authors examine how to properly use classification methods in intrusion detection for MANETs. In order to do so they evaluate five supervised classification algorithms for intrusion detection on a number of metrics. They measure their performance on a dataset, described in this paper, which includes varied traffic conditions and mobility patterns for multiple attacks. One of their goals is to investigate how classification performance depends on the problem cost matrix. Consequently, they examine how the use of uniform versus weighted cost matrices affects classifier performance. A second goal is to examine techniques for tuning classifiers when unknown attack subtypes are expected during testing.