Hybrid neural network and C4.5 for misuse detection

Intrusion detection technology is an effective approach to dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid neural network and C4.5. The key idea is to take advantage of different classification abilities of neural network and the C4.5 algorithm for different attacks. What is more, the model could also be updated by the C4.5 rules mined from the dataset after the event (intrusion). We employ data from the third international knowledge discovery and data mining tools competition (KDDcup '99) to train and test feasibility of our proposed model. From our experimental results with different network data, our model achieves more than 85 percent detection rate on average, and less than 19.7 percent false alarm rate for five typical types of attacks. Through the analysis after-the-event module, the average detection rate of 93.28 percent and false positive rate of 0.2 percent can respectively be obtained.