Network Intrusion Detection Algorithm Based on Active Learning and Semi-supervised Learning

In order to improve the classification performance of network intrusion detection, a kind of intrusion detection algorithm is proposed based on active learning and semi-supervised. The active learning algorithm was simplified, and two classifiers were trained with labeled samples to predict the unlabeled sample. The unlabeled samples that were predicted differently by the two classifiers were considered as rich information samples, and were labeled using a semi-supervised learning algorithm and were added to the training set of active learning and semi supervised learning to train classifier. The iteration was repeated until the unlabeled set was empty. The final classifier was trained and generated by the newest labeled training set. The experiment was carried out on KDD CUP 99 data set. The results show that the classification rate increased by 4.3% and the problem scale was reduced greatly.