Abstract:

Aiming at the problem of low detection rate existed in current intrusion detection systems,an adaptive evolutionary neural network algorithm (AENNA) based on the genetic algorithm and the BP algorithm is proposed.Firstly,considering the characteristic of probabilistic jumping property and local search ability in the simulated annealing algorithm,the genetic algorithm is improved.Secondly, using the dual population evolution rule,the algorithm optimizes the weight and the network structure of the BP neural network simultaneously.An adaptive neural network training method is designed through improving the crossover operator and mutation operator of the genetic algorithm.Experimental results show that the AENNA based intrusion detection method can effectively improve the detection rate and reduce the false positive rate.

Key words: genetic algorithm;neural network;simulated annealing algorithm;intrusion detection