Design of Network Security Assessment and Prediction Model Based on Improved K-means Clustering and Intelligent Optimization Recurrent Neural Network

Aiming at the security problems in cyberspace, the study proposes a cyber security assessment and prediction model based on improved K-means and intelligent optimization recurrent neural network. Firstly, based on traditional self-encoder and K-means algorithm, sparse self-encoder and K-means++ algorithm are proposed to build a cyber security posture assessment model based on improved K-means. Then, a two-way gated loop unit is used for security posture prediction, and a particle swarm optimization algorithm is utilized for enhancing the two-way gated loop unit, and the prediction is performed jointly with the model based on convolutional neural network. The results show that the proposed safety assessment model can react quickly when a fault occurs and is not prone to misjudgment with good stability. The accuracy of the safety assessment model was 99.8%, the running time was 0.277 s, and the recall rate was 96.67%, which was 96.49% in the F1 metric. The proposed safety prediction model has the lowest mean absolute error and root mean square error, which are 0.18 and 0.30. The running time is relatively long, which is 703.23 s and 787.46 s, but still within the acceptable range. The model-predicted posture values fit well with the actual posture values. In summary, the model constructed by the study has a good application effect and helps to ensure the security of cyberspace.

@article{Wang2024,
  title     = {Design of Network Security Assessment and Prediction Model Based on Improved K-means Clustering and Intelligent Optimization Recurrent Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Qianqian Wang and Xingxue Ren and Lei Li and Huimin Peng},
  doi       = {10.14569/IJACSA.2024.01506131},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506131}
}