基于改进的PAM算法的入侵检测方法

研究了入侵检测中算法的应用问题,由于PAM算法的入侵行为检测对大的数据集合没有良好的可伸缩性,提出了一种基于改进的PAM算法的入侵检测方法。首先将训练数据集转换为标准的单位特征度量空间;然后利用改进算法对数据进行划分,以找到聚类中心;最后对算法进行了性能分析与比较,并将该方法成功应用于入侵检测的仿真实验中。实验结果表明,算法具有良好的稳定性,能够有效地检测真实网络数据中的入侵行为,对大数据集合具有较好的可伸缩性。

New Intrusion Detection Method Based on Improved PAM

The applied problem in PAM algorithm is studied,because PAM algorithm has bad scalability on large dataset,the paper given out a new intrusion detection method based on PAM.Firstly the training data set is converted to the standard unit features metric space;then the improved algorithm is used to divide the data in order to find the clustering center;in end of this paper the improved algorithm is analyzed and compared with old algorithm.Experimental results show that the improved algorithm has good stability and can detect intrusions in real network data effectively.It has better scalability on large data set.