An Intelligent Method for Intrusion Detection Using Genetic-Chaos Algorithm and RBF

Improper structure parameters of Artificial Neural Network (ANN) may lead to the low precision for intrusion detection. In order to overcome this problem, a new detection method based on Genetic Algorithm (GA)-Chaos optimization and Radial Basis Function (RBF) neural network is proposed. The GA-Chaos was used to optimize the structure of the RBF as well as its weight values to obtain high learning and generalization ability of the RBF-detected model. Then the RBF model was employed to train and test the intrusion data sets. Experimental results show the method promotes the detection rate and calculation speed, and outperform the traditional GA-based methods.