NDLI: A network abnormal flow analysis method based on improved SOM

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A network abnormal flow analysis method based on improved SOM

Content Provider	IEEE Xplore Digital Library
Author	Jinyan Zhao Liqun Xi Yue Gao
Copyright Year	2010
Description	Author affiliation: College of Computer Science and Technology, Beihua University, Jilin 132013, China (Jinyan Zhao; Liqun Xi; Yue Gao)
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.
File Size	762307
File Format	PDF
ISBN	9781424472352
e-ISBN	9781424472376
DOI	10.1109/ICCASM.2010.5622176
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Algorithm design and analysis Analytical models Abnormal flow Neurons Self-organizing maps Artificial neural networks Computer applications Neural network Clustering
Content Type	Text
Resource Type	Article

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