The Research And Application Of Honeypot Technology In Network Security

With the rapid development of computer and communication technology, computer network has wound its way into all aspects of our life for its intelligent, convenient, all-weather, global features, which carries important information. With the rapid expansion of computer network, its security has become the core issue that can not be ignored in the internet development.In the process of traditional network security defense, we usually adopt the passive defensive measures, such as the firewall to block attacks. IDS to detect attacks, etc. Although they enhance the security of computer network in some extent, they still can not achieve all aspects of protection, so we use the warning technology to monitor attacks, and take measures to improve network security.Honeypot is an active security defense technology. It can be used in security warning system and meet the accurate warning, precise orientation and rapid response requirements. In this thesis, the core of the current honeypot technology was analyzed. For its defects, the RBF neural network is applied to honeypot and a new learning algorithm based on genetic ANN is proposed. It realizes parallel research and improves the accuracy of warning and self-learning ability.A new waring model based on ARIMA model is designed in this thesis. According to this model, a honeypot warning system is achieved. Finally, the data is analyzed through simulation experiment. The compate between the traditional model of honeypot and this model is made at end of the thesis. The experimental results show that the improved honeypot technology in warning has a excellent performance.