摘要
本文将自组织映射神经网络与学习矢量量化的学习算法混合,用于基于程序行为的本机入侵异常检测中。考虑到目前很多方法存在成功率低以及训练时间长的缺点,本文利用自组织映射神经网络对数据聚类,然后通过学习矢量量化对已聚类的数据再进行分类。仿真实践证明,这种混合优化技术可使分类的边界得以收缩,可提高分类精度和准度,提高了入侵检验的成功率。
This paper proposes an advanced method which was mixed by self-organizing map neural network and learning vector quantization algorithm,and used in intrusive detection system based on program behavior of local machine. For improving its low success possibility and long training time,this paper makes use of self-organizing map neural network to cluster data and then classifies these data by learning vector quantization.Using a simulation example,the mixed method not only cleared the edge of classification, advanced precision and accuracy of classification,but also improved its success possibility of intrusive detection.
出处
《微计算机信息》
北大核心
2006年第12X期92-94,147,共4页
Control & Automation
基金
陕西省自然科学基金98X11
陕西省教育厅重点科研计划项目00JK015资助
关键词
SOM
LVQ
异常检测
聚类
分类
SOM,LVQ,intrusive detection,clustering,classification
