计算机科学 ›› 2012, Vol. 39 ›› Issue (4): 106-109.
• 计算机网络与信息安全 • 上一篇 下一篇
刘永忠,李欣娣,李杨,张为群
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摘要： 传统FCM算法对初值的依赖性过大且欧氏距离只适用于处理数值型及特征空间为超球结构的数据集。为 此，利用模糊粗糙集思想，结合Relief F技术，提出了一种基于模糊粗糙集的特征加权聚类算法(FRS-FCM)，并将此算 法应用到集成入侵检测中，通过有效地聚类和集成学习来提高入侵检测的检测率，降低误检率，并较大地提高低频攻 击的检测率。最后利用KDD Cup 99数据集进行的仿真实验验证了该方法的可行性与有效性。
关键词: 模糊粗糙集，ReliefF, FRS-FCM，集成入侵检测
Abstract: Traditional FCM algorithm is too dependent on initial distance and Euclidean distance is only applied to han- dle the dataset of numeric and spatial data structure for the super-ball. Based on fuzzy rough sets and ReliefF technolo- gy, the author proposed a fuzzy rough set based clustering algorithm(FRS-FCM) , and used it to integrated intrusion de- tection. By effective clustering and integrated learning, the algorithm can improve the detection rate and reduce the false detection rate, improve the detection rate of low-frequency attacks. Finally, simulation experiments using KDl)Cup 99 data set verify feasibility and effectiveness of the algorithm.
Key words: Fuzzy rough sets, ReliefF, FRS-FCM, Integrated intrusion detection
刘永忠,李欣娣,李杨,张为群. 一种基于FRS-FCM算法的集成入侵检测方法的研究[J]. 计算机科学, 2012, 39(4): 106-109. https://doi.org/
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