An Evaluation Model for Network Security Based on an Optimized Circular Algorithm
DOI:
https://doi.org/10.13052/jcsm2245-1439.1255Keywords:
Recurrent neural networks, computer network security assessment, gaussian processes, intrusion detection, grey wolf optimizationAbstract
With more and more control systems accessing computer networks, the increase in their associated vulnerabilities has led to a decreasing security evaluation of the networks. It is essential to secure computer networks from attacks. To this end, the study constructs a network of computer network security evaluation model based on an optimized circular algorithm. To avoid detecting the model’s parameters falling into the local optimum, the model is first optimized based on the Corsi grey wolf optimization (CGWO) algorithm for the hyperparameters of the Gaussian process (GP). To solve the problem of unbalanced data and the GP not having memory capability, the study proposes an optimized Gaussian Mixture Model-Recurrent neural networks (GMM-RNN) algorithm. Experimental results of attack type recognition accuracy showed that the research CGWO-GP algorithm can jump out of the local optimum, and its average value of accuracy reached 98.99%. The average value of the leakage rate was 0.42%, and the average value of the false alarm rate was 0.11%. The average detection accuracy of the GMM-RNN model for eight attack types was 95.899%. The optimal detection accuracy of this model performance was 96.3948%. The training time of the GMM-RNN model was 67.96 s, and the detection time of the test set was 6.45 s, which greatly optimized the real-time performance. The GMM-RNN model was more effective in predicting the security posture of computer networks, and the prediction value can reach 97.65%. The research model was significantly better than other algorithmic models in the performance and evaluation of computer network security and had certain research values.
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