Fusion of multiple classifiers for intrusion detection in computer networks
Author:
Publisher
Elsevier BV
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software
Reference22 articles.
1. Allen, J., Christie, A., Fithen, W., McHugh, J., Pickel, J., Storner, E., 2000. State of the Practice of Intrusion Detection Technologies. Tech. Rep. CMU/SEI-99-TR-028, Software Engineering Institute, Carnegie Mellon University
2. The base-rate fallacy and the difficulty of intrusion detection;Axelsson;ACM Trans. Informat. Syst. Security,2000
3. Bonifacio, J.M., Cansian, A.M., de Carvalho, A.C.P.L.F., Moreira, E.S., 1998. Neural Networks applied in intrusion detection systems. Proc. IEEE World congress on Comp. Intell. (WCCI’98)
4. Cannady, J., 2000. An adaptive neural network approach to intrusion detection and response. Ph.D. Thesis, School Comp. Inf. Sci., Nova Southeastern University
5. Debar, H., Becker, M., Siboni, D., 1992. A Neural Network Component for an Intrusion Detection System. Proc. IEEE Symp. Res. Security Privacy, Oakland, CA, USA, pp. 240–250
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