NAT Recognition Technology Based On Projection Pursuit

With the sustained rapid development of Internet, the number of devices connected to the Internet is growing constantly, and also has brought the problem of the shortage of IP address. NAT technology effectively solved the problem of the lack of IP address resources, and realize the private network and public network IP address visits, but also brought private network equipment invisible security hidden danger.Because most existing methods rely on a specific field in IP packets, we propose a NAT detection method based on projection pursuit through the analysis of network traffic data. Then we can divide NAT devices and non-NAT devices through collecting the characteristic parameters of the network traffic data.This topic in the projection pursuit clustering model in quantum particle swarm optimization(QPSO) is introduced into the analysis of high-dimensional observation data, the quantum particle swarm optimization(QPSO) is based on quantum mechanics is a new PSO algorithm is put forward. Relative to the basic PSO, the algorithm process generally consistent, but the number of parameters is less, update the formula of particle location algorithm is also different. Better search ability and relative to the PSO algorithm. Topic using QPSO rapid global convergence of the algorithm and the projection pursuit model of dimension reduction ability, to deal with high-dimensional data, the results into low-dimensional subspace, by finding that can reflect characteristics of the structure of the original high-dimensional data or projection, so as to realize the research and analysis of high-dimensional data.In subject, respectively for NAT technology, NAT feature parameters selection, projection pursuit algorithm, the standard particle swarm algorithm(PSO) and the principle of quantum particle swarm optimization(QPSO) and related implementation process is introduced and analyzed, and through the experiment on the data collected by the projection pursuit algorithm based on quantum particle swarm optimization(PSO) algorithm, the projection values projected onto two-dimensional coordinate is analyzed.