基于克隆选择聚类的入侵检测

提出基于克隆选择的模糊聚类算法，将该聚类算法用于网络入侵检测。针对入侵数据的混合属性改进距离测度的计算方法，实现了对大规模混合属性原始数据的异常检测，并能有效检测到未知攻击。在KDDCUP99数据集中进行了对比仿真实验，实验结果表明算法对已知攻击和未知攻击的检测率以及算法的误誊率都是理想的。

Intrusion Detection Based on CSA Clustering Algorithm

The clustering algorithm is employed for the network to detect the intrusions in this paper. And in order to treat the data set with mixed numeric and categorical values, a novel algorithm for mixed data by modifying the common cost function and race of the within cluster dispersion matrix is used here. So the intrusion detection system can deal with mass unlabeled data to distinguish between normal and anomaly and to detect unknown attacks effectively. The simulations on the KDD CUP99 dataset show that the detection rate of known attacks and unknown attacks and the false positive rate of this algorithm are excellent.