摘要
针对基于传统模糊C均值聚类的网络入侵检测模型存在分类效果不佳,且容易出现局部极值的问题,提出了一种基于量子人工鱼群的半监督模糊核聚类算法。该算法使用少量的标记数据和大量未知标记数据生成网络入侵检的分类,并通过核距离的方式构建了模糊C均值聚类算法的新目标函数,此外,结合了量子人工鱼群算法来解决模糊核聚类算法的全局最优解问题,适用于并行执行架构。在KDDCup99网络入侵检测数据上的仿真实验结果表明,相比于基于FCM和PSO-FCM的入侵检测模型,以及基于此提出的算法入侵检测模型具有更好的检测率。
Aiming at the problem that the network intrusion detection model based on traditional fuzzy C-means clustering has poor classification effect and the local extremum is easy to occur,the paper proposes a semi-supervised fuzzy kernel clustering algorithm based on quantum artificial fish school optimization.The algorithm uses a small amount of tag data and a large amount of unknown tag data to generate the classification of network intrusion detection,and constructs a new objective function of fuzzy C-means clustering algorithm by means of kernel distance.In addition,it is combined with quantum artificial fish school optimization algorithm to solve the global optimal problem of the fuzzy kernel clustering algorithm,which is applicable to the parallel execution architecture.The simulation results on the KDD Cup 99 network intrusion detection data show that the intrusion detection model based on the proposed algorithm has better detection rate than that based on FCM and PSO-FCM.
作者
李根
LI Gen(Department of Computer Application Technology,Guangdong College of Business and Technology,Zhaoqing 526020,China)
出处
《软件工程》
2019年第6期33-37,共5页
Software Engineering
关键词
网络安全
入侵检测
量子人工鱼群
半监督学习
C均值聚类
network security
intrusion detection
quantum artificial fish school
semi-supervised learning
C-means clustering
