Abstract:

Network security is facing increasingly complex challenges. With the diversification of attack methods and types, the extent of damage is also increasing; network protection requirements have been from a single passive approach to data fusion of active network technology under the situation awareness. Therefore, for the study of abnormal data classification is still very important. However, the traditional classification algorithm in the face of unbalanced data, only consider the algorithm accuracy, ignoring the classification effect of the minority class, thus easily lead to attacks and vulnerabilities of false positives, and for the new type of abnormal recognition efficiency is not ideal. Aiming at the above problems, firstly, this paper uses the sampling method of active learning algorithm to improve the learning efficiency in a large number of samples; then, the classification algorithm is improved based on the idea of the combination classifier, and the classification accuracy of the algorithm is increased by using the misclassification cost function; finally, the feasibility and effectiveness of the proposed method are verified by comparing the proposed method with the traditional method.

Key words: network security, imbalanced classification, active learning, cost function, combination classification