Research Article
Alarm Elements Based Adaptive Network Security Situation Prediction Model
@INPROCEEDINGS{10.1007/978-3-030-66922-5_3, author={Hongyu Yang and Le Zhang and Xugao Zhang and Guangquan Xu and Jiyong Zhang}, title={Alarm Elements Based Adaptive Network Security Situation Prediction Model}, proceedings={Security and Privacy in New Computing Environments. Third EAI International Conference, SPNCE 2020, Lyngby, Denmark, August 6-7, 2020, Proceedings}, proceedings_a={SPNCE}, year={2021}, month={1}, keywords={Network situation Alarm element Entropy correlation Cubic exponential smoothing Time-varying weighted Markov Predicated value}, doi={10.1007/978-3-030-66922-5_3} }- Hongyu Yang
Le Zhang
Xugao Zhang
Guangquan Xu
Jiyong Zhang
Year: 2021
Alarm Elements Based Adaptive Network Security Situation Prediction Model
SPNCE
Springer
DOI: 10.1007/978-3-030-66922-5_3
Abstract
To improve network security situation prediction accuracy, an adaptive network security situation prediction model based on alarm elements was proposed. Firstly, we used the entropy correlation method to generate the network security situation time series according to Alarm Frequency (AF), Alarm Criticality (AC) and Alarm Severity (AS). Then, the initial situation predicted value is calculated through sliding adaptive cubic exponential smoothing. Finally, based on the error state, we built the time-varying weighted Markov chain to predict the error value and modify the initial predicted value. The experimental results show that the network security situation prediction results of this model have a better fit with the real results than other models.
