Cost-sensitive classification strategy for imbalanced datasets of intrusion detection
不平衡入侵检测数据的代价敏感分类策略*

This paper proposed a new data preprocessing algorithm called AdaP for avoiding over-fitting effectively while processed independently. In view of the imbalanced datasets, introduced the cost-sensitive mechanism into the intrusion detection system by the ideas: weighting matrix to minimize the misclassification costs, removing some datum in dense region expanding in rare region, as well as filtering noises. At last combined the date preprocessing algorithm AdaP with the boosting algorithm AdaCost successfully. The experiment fully reflects the advantages of boosting the classification precision with weak leaner to balance rare classes and shows the strategy which can improve classification performance of the intrusion detention in terms of the imbalanced datasets immensely.