关键词: 支持向量机, 网络异常检测, 投票委员会, 协同标注

Abstract: This paper focuses on the imbalanced data classification and the labeling cost, proposes a classification method DC-TSVM (dynamic cost and cooperative labeling transductive support vector machine) based on voting committee algorithm and dynamic cost. This method constructs each sub-classifier according to the misclassification cost of each sub-class that is calculated based on the relationship of density. It can reduce the offset of the classified hyperplane. Then this method labels the sample according to the voting entropy. It can reduce the accumulation and transmission of errors, improve the accuracy of labeling and get the high?generalization?performance. Finally, the experimental results with KDDCUP99 dataset show that this method has higher detection accuracy for unknown attacks.

Key words: support vector machine, network anomaly detection, voting committee, cooperative labeling