Application of Genetic Algorithm-Grey Wolf Optimization-Support Vector Machine Algorithm in Network Security Services Assessment and Prediction
DOI:
https://doi.org/10.13052/jcsm2245-1439.1356Keywords:
Spectral cluster analysis, Cyber security posture, GA, GWO, SVMAbstract
The continuous development of information technology has also promoted the progress of the Internet. More people are joining the Internet. The amount of data stored in the network is also increasing, including some important information, which leads to criminals launching attacks on network security. In order to solve the large error in network security situation assessment and poor progress in network security prediction, the study uses spectrum clustering analysis to evaluate the network security situation. Then genetic algorithm, grey wolf optimization algorithm and support vector machine fusion algorithm are used to predict the Network Security Service (NSS). The genetic algorithm is used to optimize the global search ability of the gray wolf optimization algorithm and the parameters of the support vector machine are optimized to evaluate and predict the NSS. The results showed that the maximum error of the proposed model was 0.4112, and the maximum error was 0.5896. The absolute percentage error of this algorithm was 0.0270, while the other algorithms were 0.0745 and 0.0952, respectively. The proposed model has lower errors and time consumption in training and simulation testing compared with other current methods. The network situation assessment and prediction method proposed in the study can effectively improve network security services, ensure the personal information security, and enhance the security of the Internet.
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