The intrusion detection problem could be modeled as data stream classification problem.For traditional data stream classification algorithms

a lot of labeled training samples should be provided to train the classifier

with a high expense

and decrease the utility of the algorithms.For positive unlabeled learning (PU Learning) based algorithms

only positive and unlabeled samples are required to train classifier.In this paper

we propose to use dynamic classifier ensemble of PU learning algorithm to cope with intrusion detection

which only requires label some positive samples for building data stream classifier. The experiment result on both synthetic and real-life dataset shows that the proposed algorithm has excellent classification performance

and it outperforms 3 PU learning algorithms on data streams with shewed distribution

with high intrusion detection ratio.