Name
Title | ||
|---|---|---|
A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm. |
Abstract | ||
|---|---|---|
Display Omitted The hidden BRB model is used to predict the network security situation.The observation data of the hidden BRB model is multidimensional.We propose a new constraint CMA-ES algorithm.The revised CMA-ES algorithm is used to optimize the parameters of the hidden BRB model. It is important to establish the forecasting model of the network security situation. But the network security situation cannot be observed directly and can only be measured by other observable data. In this paper the network security situation is considered as a hidden behavior. In order to predict the hidden behavior, some methods have been proposed. However, these methods cannot use the hybrid information that includes qualitative knowledge and quantitative data. As such, a forecasting model of network security situation is proposed on the basis of the hidden belief rule base (BRB) model when the inputs are multidimensional. The initial parameters of the hidden BRB model given by experts may be subjective and inaccurate. In order to train the parameters, a revised covariance matrix adaption evolution strategy (CMA-ES) algorithm is further developed by adding a modified operator. The revised CMA-ES algorithm can optimize the parameters of the hidden BRB model effectively. The case study shows that compared with other methods, the proposed hidden BRB model and the revised CMA-ES algorithm can predict the network security situation effectively to improve the forecasting precision by making full use of qualitative knowledge. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.asoc.2016.05.046 | Appl. Soft Comput. |
Keywords | Field | DocType |
Network security situation prediction,Hidden behavior,Belief rule base (BRB),Covariance matrix adaption evolution strategy (CMA-ES),Modified operator | Data mining,Observable,Computer science,Network security,Algorithm,Evolution strategy,Operator (computer programming),Artificial intelligence,CMA-ES,Covariance matrix,Qualitative knowledge,Machine learning | Journal |
Volume | Issue | ISSN |
48 | C | 1568-4946 |
Citations | PageRank | References |
6 | 0.48 | 17 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guan-Yu Hu | 1 | 10 | 4.24 |
| ZhiJie Zhou | 2 | 479 | 37.36 |
| Bang-Cheng Zhang | 3 | 61 | 9.39 |
| Xiao-Jing Yin | 4 | 17 | 4.40 |
| Zhi Gao | 5 | 6 | 0.48 |
| Zhiguo Zhou | 6 | 27 | 2.94 |