Existing anomaly intrusion detection algorithms based on supervised learning usually face difficulties when the training samples are insufficient.At the same time

the characteristics that the pattern of network traffic is changing over time are not considered adequately by the intrusion detection algorithms which are based on the equivalent learning of all historical dataset.So

a streaming ensemble intrusion detection model based on small labeled data is presented

in which a small labeled dataset is extended to work out the problem of the insufficiency of training samples.In addition

it can adapt to the changes of network traffic adequately.The experimental results manifest that the algorithm has better detection performance than those based on all historical data while the size of labeled dataset is very small.