Application of BP Neural Network Optimized by Artificial Bee Colony in Intrusion Detection
摘要
The existence of traditional BP neural network is easy to fall into local optimum,slow convergence and other shortcomings.According to the features of global optimization and swarm intelligence of Artificial Bee Colony(ABC) algorithm,in the neural network parameter initialization,this paper uses the deviation of the neural network as a fitness of ABC algorithm,selects the best fitness of a set of parameters as a nerve power networks and thresholds.Doing so can avoid falling into local optimum neural network and slow convergence problem.The BP neural network model of ABC optimization applied to intrusion detection.Simulation results show that the network model is optimized to accelerate the convergence rate and improve the detection accuracy.