Intrusion Detection Based on Adaptive RBF Neural Network

Jiang Zhong, Zhiguo Li, Yong Feng, Chunxiao Ye

Abstract: Recently the machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, we propose a new method to design classifier based on multiple granularities immune network. Firstly a multiple granularities immune network (MGIN) algorithm is employed to reduce the data and get the candidate hidden neurons and construct an original RBF network including all candidate neurons. Secondly, the removing redundant neurons procedure is used to get a smaller network. Experimental results on the real network data set show that the new classifier has higher detection and lower false positive rate than traditional RBF classifier