As an active and dynamic security-defense technique, intrusion detection can detect the interior and exterior attacks, and it plays an important role in assuring the network security. Based on immune recognition algorithm, a Radial Basis Function (RBF) neural network learning algorithm was studied. In this algorithm, the input data is regarded as antigens and antibodies are regarded as the hidden layer centers, the weights of the output layer are determined by adopting the Recursive Least Square method, which can improve convergence speed and precision of the RBF neural network, using Snort to establish innate antibody and using negative selection algorithm to generate detectors. This algorithm was applied to Intrusion Detection Systems. Theory and experiment show that this algorithm has better ability in intrusion detection, and can be used to improve the efficiency of intrusion detection, and reduce the false alarm rate.
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