An approach to network intrusion detection algorithm based on BP-HMM

A network intrusion detection framework and its associated algorithm based on BP-HMM are put forward; the training and recognition methods of the algorithm are given. A sheer classifier based on HMM can't give attention to both the strong recognition ability for corresponding objects and maximization of difference lain in different models, so a BP neural network is used to provide state probability output for HMM in the HMM framework. Because of the coarse classification of BP, the limitation of HMM is overcome; the ability of classification and recognition is enhanced. Through the use of the any-path method, the accurate rate of recognition is not only improved, but also an obvious calculation predominance is obtained.