Research Article
Optimization Design of Large-Scale Network Security Situation Composite Prediction System
@INPROCEEDINGS{10.1007/978-3-030-36405-2_42, author={Jinbao Shan and Shenggang Wu}, title={Optimization Design of Large-Scale Network Security Situation Composite Prediction System}, proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2019}, month={11}, keywords={Large-Scale Network security situation compound forecast Forecast result Accuracy}, doi={10.1007/978-3-030-36405-2_42} }- Jinbao Shan
Shenggang Wu
Year: 2019
Optimization Design of Large-Scale Network Security Situation Composite Prediction System
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-36405-2_42
Abstract
Because the traditional network security situation compound prediction system cannot overcome the defects of SVM algorithm, the accuracy of extraction results is low. For this reason, a large-scale network security situation compound prediction system is designed. Through data normalization process to optimize the SVM algorithm, to optimize the forecasting calculation module, to provide data base system frame structure, system frame structure can be divided into security situational composite sensing module, situational composite evaluation module and situational composite prediction module, synergy is derived using multiple module network security situational values when attacked, to implement network security situation prediction to complete the system design. Simulation application environment design compared the experimental results show that compared with the traditional prediction system of the proposed system under the same data to forecast, the accuracy of predicted results by 65%, and the operation is very stable.
