关键词: 半监督学习, 集成学习, 入侵检测

Abstract: Regularization self-training is a new method based on ensemble learning. It can solve the problem of insufficient labeled training samples in intrusion detection. The proposed algorithm combined active learning and regularization theory, and utilized unlabeled data to improve the existing classifiers. The experiments were running on three main ensemble learning algorithms under different unlabeled rate. The results prove that the proposed method can improve the boundary of the ensemble classifiers, and reduce the error rate with the help of large amounts of unlabeled data.

Key words: semi-supervised leaning, ensemble learning, instruction detection