Intrusion Detection for Transportation Information Security Systems Based on Genetic Algorithm-Chaos and RBF Neural Network

The transportation information security system plays an important role in the development of traffic information construction. Improper structure parameters of ANN may lead to low precision for intrusion detection of the transportation information security system. In order to overcome this problem, a new detection method based on GA-Chaos optimization and RBF neural network is proposed. The GA-Chaos was firstly 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 standard GA based methods.