基于自适应免疫分类器的入侵检测

由于采用传统的分类器进行检测时,存在检测率低而误报率高的问题.提出了一种基于免疫聚类的自适应分类器方法,采用多信息粒度的思想有效地克服了聚类算法与分类算法间的不一致性.通过在真实网络数据集上对多种入侵行为的检测结果表明:该分类器的检测率高、漏报率和误报率低,较RBF分类器和BP分类器具有更好的分类性能和推广性能.

Intrusion Detection Based on Adaptive Immune Classifier

The distribution properties of the normally data and anomaly data in the network connectivity features have huge differences ; therefore, there exist the low rate of detection and false positive rate problem for the traditional classifier which is applied to the network intrusion detection. An adaptive classifier based on the artificial immune cluster is presented. The new classifier adopts multi -granularities idea and it effectively eliminates the inconsistency between the classification algorithm and the clustering algorithm. Through the classification of the data sets in real variety of network intrusion data sets, experimental results show that the classifier has high detection rate and low false positive rate; it has better classification performance and generalization ability than RBF and BP classifiers.