Name
Abstract | ||
|---|---|---|
In the network security situation assessment based on hidden Markov model, the establish of state transition matrix is the key to the accuracy of the impact assessment. The state transition matrix is often given based on experience. However, it often ignores the current status of the network. In this paper, based on the game process between the security incidents and protect measures, we improve the efficiency of the state transition matrix by considering the defense efficiency. Comparative experiments show the probability of the network state generated by improved algorithm is more reasonable in network security situation assessment. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-981-10-0356-1_65 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Network security situation assessment,Game matrix,Defense efficiency | Impact assessment,Data mining,Computer science,Markov model,Network security,Situation analysis,Markov blanket,Artificial intelligence,State-transition matrix,Hidden Markov model,Causal Markov condition,Machine learning | Conference |
Volume | ISSN | Citations |
575 | 1865-0929 | 1 |
PageRank | References | Authors |
0.36 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuang Xiang | 1 | 1 | 0.70 |
| Yanli Lv | 2 | 1 | 1.71 |
| Chunhe Xia | 3 | 63 | 18.30 |
| Yuanlong Li | 4 | 10 | 3.24 |
| Zhihuan Wang | 5 | 1 | 0.36 |