Title :
A network abnormal flow analysis method based on improved SOM
Author :
Zhao, Jinyan ; Xi, Liqun ; Gao, Yue
Author_Institution :
Coll. of Comput. Sci. & Technol., Beihua Univ., Jilin, China
Abstract :
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.
Keywords :
self-organising feature maps; telecommunication computing; telecommunication network management; SOM neural network; best matching neurons; network abnormal flow analysis method; self-organizing feature maps; Algorithm design and analysis; Analytical models; Artificial neural networks; Computer applications; Neurons; Training; abnormal flow; clustering; neural network; neurons; self-organizing maps;
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
DOI :
10.1109/ICCASM.2010.5622176
