Abstract
With the rapid development of Internet, the network topology structure becomes more and more complex, so that the monitoring of network attack has become quite difficult. The traditional passive defence strategy has been unable to meet the demand of network information security. How to effectively detect and prevent the network intrusion have become an important matter in the field of computer security. The efficient intrusion detection system can reduce the false positive rate of the system, and improve the classification accuracy. This paper firstly introduces the intrusion detection system and detection data set. On this basis, this paper proposes an intrusion detection method based on improved PSO-SVM. The support vector machine can ensure that classifier has high classification accuracies. Secondly, we use PSO method to determine the important parameters of the SVM algorithm, such as the RBF kernel parameter, penalty parameter and insensitive loss error. Then, the improved PSO method can find the optimal value of the SVM. At this time, the error sum of squares of the SVM model has a minimum value, and the model has a fast convergence speed. Finally, because the training data sets of DoS and Probe are accounted for a larger proportion of all attacks, we use the IPSO-SVM classification algorithm for them, and have a test to the intrusion detection. The experimental results show that the overall performance of the proposed detection algorithm is very high. It has a strong ability to identify the characteristics of intrusion, and can provide the intrusion detection services for virtual environment.
