基于改进RBF神经网络的入侵检测研究

Research of intrusion detection based on improved RBF neural network

近年来,神经网络技术在入侵检测中得到了广泛应用,其中最具代表的是BP神经网络,但其本身所具有的局部极小性质限制了检测性能的提高。RBF神经网络在一定程度上克服了BP神经网络存在的问题,但如何确定一个合适的RBF网络隐层神经元中心个数又是保证其应用效果的关键之一。因此,将基于熵的模糊聚类和RBF神经网络相结合,提出了基于EFC的改进RBF神经网络算法,并将该方法应用于入侵检测研究。实验表明,该算法可以获得满意的性能。

In recent years,the neural network technology obtained the widespread application in the Intrusion,what most has represents is the BP neural network,but the local minimum nature of itself has limited the detection performance enhancement.The RBF network can solve the BP neural network existence question.But how to determine an appropriate RBF network hidden centers is also a difficult problem.ln view of the above question,by combining Entropy-based Fuzzy Clustering（EFC） and neural network,this paper puts forward an improved RBF algorithm based on Entropy-based Fuzzy Clustering and applies this algorithm to intrusion detection.The experiment shows that algorithm can gains satisfying performances.