The Research Of Intrusion Detection System Of Wlan Based On Optimized Adaptive Neural Network

With the rapid development of wireless local area network, the existence of its own security problems, has also slowly attracted attention.Intrusion detection system has become a research focus of network security as an important component of information security.Although research on cable network intrusion detection system has been relatively mature, further research on intrusion detection technology specific to wireless local area network is still required because we cannot apply existing technologies directly to wireless networks due to differences between wireless networks and cable networks.Traditional Intrusion Detection technologies are based mainly on rules and expert knowledge base and are strongly dependent on them relatively, and thus does not adapt well to the environment. As a result, this article focuses on the work of introducing the neural network technology into the wireless LAN intrusion detection system, to improve the accuracy of intrusion detection systems and real time by improving the traditional neural network algorithm.This article is outlined as the following:1)Analysis the characteristics of wireless LAN security based on the full research of existing security vulnerabilities and attacks.2)Improve the traditional BP neural network algorithm, Propose the BP neural neural network algorithm based on the minimum transition,and then design detection model based on neural network algorithm by analyzing the characteristics of wireless LAN security holes.3)Finally, design distributed multi-Agent wireless LAN intrusion detection system,add detection model based on neural network algorithm to the system,analysis the results of the simulating attacks test on the model.