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In order to solve the problem of recursion equation in network security, the author proposes an application of network security risk detection. The search efficiency of the artificial intelligence planning algorithm is better than the traditional attack graph generation method, designed and implemented a planning engine for security risk assessment, according to the application problem definition, design data processing methods and grammar translation modules; efficient planning algorithms for penetration plan planning are selected and the analysis of the risk association process is completed. First, the development status and challenges of network security are summarized, and then, the research status of existing risk assessment methods is analyzed, the research ideas are introduced, and the main research results are given; the organizational structure is listed at the end. In the optimized parallel algorithm for recursive equations, when using P processors to solve a class of recursive equations of size N, the speedup of this algorithm is 0 ( p ), where 1 &gt;  p  &gt; 0.1 is an arbitrarily small positive number. Using the advantages of neural networks dealing with nonlinearity and complexity to predict the network security situation based on the improved recurrent neural network, the experimental results prove that the proposed method has high operation efficiency, low error and high accuracy compared with the actual value.

Application of nonlinear recursion equation in network security risk detection