摘要
高校网络被外网访问时,外网访问数据没有类别标记,导致数据识别特征不明显,传统的入侵检测模型不能有效提取出无监督外网访问数据中的识别特征,无法准确训练入侵检测模型,造成高校网络入侵检测准确度不高。为了解决这一难题,提出一种基于无监督免疫优化分层的入侵检测算法,即在免疫网络中对数据进行学习,用小规模的网络完成数据压缩,集中增强数据的识别特征,运用分层聚类方法分析网络,完成数据模型的建立。仿真实验表明,这种无监督入侵检测模型方法克服了高校网络外网访问数据的识别特性不明显,提高了高校网络入侵检测的准确率,取得了满意的结果。
When the external network accesses university network, the external network data has no category tags, so the data recognition is unclear. The traditional intrusion detection model can not effectively extract the identifying char- acteristics of the unsupervised external network accessing data, and intrusion detection model can not be accurately trained,which makes accuracy of the college network intrusion detection is not high. To solve this problem, this paper proposed a intrusion detection algorithm based on unsupervised immune hierarchical optimization to learn data in the immune network, complete the data compression using the small-scale network,focus on improving the identifying cha- racteristics of the data, and analyze the network using hierarchical clustering method to complete the establishment of the data model. Simulation results show that this unsupervised intrusion detection model method overcomes the obvious identifying characteristics of the university external network accessing data, and improves the accuracy of the university network intrusion detection,achieves satisfactory results.
出处
《计算机科学》
CSCD
北大核心
2013年第3期180-182,191,共4页
Computer Science
基金
浙江省自然科学基金(Y1101237)资助
关键词
高校网络
入侵检测
无监督
免疫网络
University network, Intrusion detection,Unsupervised, Immune network
