Network Security Situation Prediction Method Based on Support Vector Machine Optimized by Artificial Bee Colony Algorithms

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The validity and accuracy of the network security situation prediction algorithm is of great significance to network security. Aiming at the shortcomings of the basic artificial bee colony algorithm, such as easy to fall into the local optimal solution and slow convergence in the late stage of the algorithm, this paper proposes a network security situation prediction model based on support vector machine(SVM) optimized by improved artificial bee colony algorithm(I-ABC), using I-ABC algorithm for SVM. The penalty factor a and the kernel parameter b are optimized. Finally, the simulation test is performed using real network security situation data. The simulation results show that the proposed algorithm can accurately track the change of situation value and effectively improve the prediction accuracy of network security situation.