Intrusion detection using a cascade of boosted classifiers (CBC)

A boosting-based cascade for automatic decomposition of multiclass learning problems into several binary classification problems is presented. The proposed cascade structure uses a boosted classifier at each level and use a filtering process to reduce the problem size at each level. The method has been used for detecting malicious traffic patterns using a benchmark intrusion detection dataset. A comparison of the approach with four boosting-based multiclass learning algorithms is also provided on this dataset.