Study on the Network Intrusion Detection Model Based on Genetic Neural Network

According to the high missing report rate and high false report rate of existing intrusion detection systems, the paper proposed an anomaly detection model based on genetic neural network, which combined the good global searching ability of genetic algorithm with the accurate local searching feature of BP Networks to optimize the initial weights of neural networks. The practice overcame the shortcomings in BP algorithm such as slow convergence, easily dropping into local minimum and weakness in global searching. Simulation results showed that the practice worked well and learnt fast and had high-accuracy categories.