The method of multiple neural network Fusion using Fuzzy Integral (MNNF) presented by this paper is to improve the detection performance of data mining-based intrusion detection system. The basic idea of MNNF is to mine on distinct feature training dataset by neural networks separately, and detect TCP/IP data by different neural networks, and then nonlinearly combine the results from multiple neural networks by fuzzy integral. The experiment results show that this technique is superior to single neural networks for intrusion detection in terms of classification accuracy. Compared with other combination methods such as Majority, Average, Borda count, fuzzy integral is better than one of them.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.