子空间聚类在入侵特征选择中的应用

Application of subspace clustering in intrusion feature selection

子空间聚类能在高维空间挖掘隐藏在不同低维子空间中的簇类,能在分类的基础上有效降维。针对目前入侵检测实时性和准确性的要求,提出子空间聚类ASCOD算法,该算法内嵌离群点扫描处理,能动态计算最优的算法参数,将该算法应用于入侵特征选择领域,实验结果证明这种策略的抗干扰能力较强,并能高效进行特征选择,提高了入侵检测的检测速度和精度。

Subpace cluster can not only mine, in the high-dimension, cluster classes hidden in different low-dimensions, but also effectively reduce dimensionality based on classification. ASCOD algorithm of subspace cluster is brought forward for the requirements of real-time characteristic and accuracy in intrusion detection. Scan processing of outlier is included into this algorithm, which can dynami- cally calculate the optimal parameters. The algorithm is applied to the field of intrusion feature selection, experimental results show that it can carry out its strong ability against distraction and effectively select feature, thus improving the testing speed and accuracy of intrusion detection.