The Application of Genetic Neural Network in Network Intrusion Detection

Traditional network security models have not meet the development of network technologies, so PPDR model emerged, as the times require. Instruction detection technology is an important composed part in PPDR model and it make up for the shortages of firewall and data security protection. This technology has not only distinguished from computer and network resources, but also has given important information in instruction; it has not only detected instructing action from out word, but also has controlled user's actions. Instruction detection technology is the core in instruction detection system, it include abnormity instruction and abused instruction detection. However, how to detect whether there are intrusions is a problem to need solving first. According to the high missing report rate and high false report rate of existing intrusion detection systems, the paper proposed an anomaly intrusion 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, fast learning speed and high-accuracy categories.