A global optimization technique for statistical classifier design
IEEE Transactions on Signal Processing
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
An ART-based construction of RBF networks
IEEE Transactions on Neural Networks
A self-organizing HCMAC neural-network classifier
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Neuron selection for RBF neural network classifier based on data structure preserving criterion
IEEE Transactions on Neural Networks
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In this paper, we propose a novel Intrusion Detection algorithm utilizing both Artificial Immune Network and RBF neural network. The proposed anomaly detection method using multiple granularities artificial immune network algorithm to get the candidate hidden neurons firstly, and then, we training a cosine RBF neural network base on gradient descent learning process. The principle interest of this work is to benchmark the performance of the proposed algorithm by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. It is observed that the proposed approach gives better performance over some traditional approaches.