基于层次自组织特征映射的网络异常检测系统数据分析器

A DATA ANALYZER IN NETWORK ANOMALY DETECTION SYSTEM BASED ON HIERARCHICAL SELF-ORGANIZING MAP

提出将层次自组织特征映射神经网络算法应用于网络异常检测,算法自顶向下逐层生成神经网络结构并细化聚类,将神经元的组织和连接方式从平面扩展到层次与平面连接相结合,大大加速了获胜神经元的搜寻过程。基于此种算法,设计并实现了网络异常检测系统中的数据分析器HSOMDA,在DARPA 1999数据集上的实验表明其具有较高的检测性能和时间性能。

This paper presents an application of a hierarchical self-organizing map （HSOM） algorithm for network anomaly detection. By growing in a top-down approach and extending the connections of neurons from horizontal dimension to both horizontal and hierarchical dimension, HSOM significantly accelerates the searching of winning neurons. Based on HSOM, a data analyzer in network anomaly detection system HSOMDA is designed and implemented. The results of experiments on DARPA 1999 dataset demonstrate its effectiveness and efficiency in anomaly detection.