摘要：According to the energy constrained, low-storage space and limited computing ability of wireless sensor network nodes, an intrusion detection model based on GA-LMBP was proposed. Compared with traditional methods, the program takes advantage of offline learning neural network algorithm to build detection model without storing large amounts of intrusion features, saving storage resources. Compared with the use of promiscuous mode capturing data, multi-detection cooperative mechanism reduces energy consumption. Simulation results show that GA-LMBP intrusion detection model in terms of performance, energy consumption, storage costs, the detection rate and false detection rate is better than those of traditional methods.
