A Content-Based Self-Feedback E-government Network Security Model

Based on the theories of the intrusion trapping and natural language understanding, oriented e-government affairs security issues, this paper proposed a content-based self-feedback model at the point of attackers. By this model, the concrete information under attacking can be focused and the attack methods would be ignored in a standard honey trap. With the supporting of honey nets, The target sensitivity is taken as the appraisement factor to demonstrate whether the information need be protected. Through adjusting the primitive feedback coefficients of the model, we can know the most important information as the attackers focusing on. At the same time, this paper introduced the concept of domain coefficients of the model. Through the experiments in the actual networks, it is the first successful model being of the prediction and feedback for e-government affairs.