ISSA-ELM: A Network Security Situation Prediction Model

Abstract

To resolve the problems of low prediction accuracy and slow convergence speed of traditional extreme learning machines in network security situation prediction methods, we combine a meta-heuristic search algorithm with neural networks and propose a prediction method based on the improved sparrow search algorithm optimization of an extreme learning machine. Firstly, the initial population is initialized by cat-mapping chaotic sequences to enhance the randomness and ergodicity of the initial population and improve the global search ability of the algorithm. Secondly, the Cauchy mutation and tent chaos disturbance are introduced to expand the local search ability, so that the individuals caught in the local extremum can jump out of the limit and continue the search. Finally, the explorer-follower number adaptive adjustment strategy is proposed to enhance the global search ability in the early stage and the local depth mining ability in the later stage of the algorithm by using the change of the explorer and follower numbers in each stage to improve the optimization-seeking accuracy of the algorithm. The improvement not only guarantees the diversity of the population, but also makes up for the defect that the sparrow search algorithm is easily trapped in the local optima in later iterations, and greatly improves the accuracy of the network security situation prediction.