摘要
针对模糊C-均值聚类法因对初始聚类中心敏感且容易陷入局部极小值而导致无法在网络入侵检测中获得精确分类结果的问题,提出了基于萤火虫群优化(GSO:Glowworm Swarm Optimization)算法的网络入侵检测方法。采用标记样本得到初始聚类中心,运用萤火虫群优化实现对聚类中心的优化。结果显示该方法有效。
Because fuzzy C-means clustering method is sensitive to initial cluster centers and easily trapped into local minima, we can＇t get precise classification result in network intrusion detection. To solve the problem, a network intrusion detection method based on GSO（ Glowworm Swarm Optimization） algorithm is proposed. First, samples with label is used to get initial cluster center. Then, GSO is employed to optimize cluster center. Simulation result shows that the method is effective.
出处
《吉林大学学报（信息科学版）》
CAS
2015年第3期338-343,共6页
Journal of Jilin University（Information Science Edition）
关键词
萤火虫群优化算法
网络入侵
模糊C-均值聚类
半监督
glowworm swarm optimization （GSO） algorithm
network intrusion
fuzzy C-means clustering
semi-supervised
