A Network Abnormal Flow Analysis Method Based on Improved SOM

摘要：

A network abnormal flow analysis method based on improved SOM neural network is proposed in this paper. This method uses the known characteristic flow data to train the SOM neural network, and mark normal flow data and abnormal flow data clustering neurons according to training results. According to the best matching neurons of the testing data to judge whether the abnormal flow happen when detecting. To verify the effectiveness of tests, using the KDD cup99 evaluation database as the network training and test data, the detection results of abnormal flow detecting methods based on improved SOM is compared with the detection method based on classic SOM. Simulation experimental results show that the analysis method based on improved SOM have high detecting rate, short training time and strong generality etc.

关键词： self-organizing maps neural network clustering abnormal flow neurons

作者: Jinyan Zhao Liqun Xi Yue Gao

作者单位: College of Computer Science and Technology, Beihua University Jilin 132013, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 33-37

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）