A Neural Networks Pruning and Data Fusion Based Intrusion Detection Model

Abstract:

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and Pruning algorithm of neural networks is used for filtering information from multi-sensors. In the process of intrusion analysis pruning algorithm of neural networks is used for filtering information from multi-sensors so as to increase its performance and save the bandwidth of networks.

• Periodical: Applied Mechanics and Materials (Volumes 651-653)
• Pages: 1772-1775
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.651-653.1772
• Online since: September 2014
• Authors: Wei Gong*
• Keywords: Data Fusion, IDS, Neural Network (NN)

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