| 1. Jajodia S, Liu P, Swarup V, et al. Cyber situational awareness. Berlin, Germany: Springer, 2010
2. Harmer P, Thomas R, Christel B, et al. Wireless security situation awareness with attack identification decision support. Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Cyber Security (CICS’11), Apr 11-15, 2011, Paris, France. Piscataway, NJ, USA: IEEE, 2011: 144-151
3. Zakrzewska A N, Ferragut E M. Modeling cyber conflicts using an extended Petri net formalism. Proceedings of the Computational Intelligence in Cyber Security (CICS’11), Apr 11-15, 2011, Paris, France. Piscataway, NJ, USA: IEEE, 2011: 60-67
4. Vu H L, Khaw K K, Chen T Y. A new approach for network vulnerability analysis. Proceedings of the 33rd IEEE Conference on Local Computer Networks (LCN’08), Oct 14-17, 2008, Montreal, Canada. Piscataway, NJ, USA: IEEE, 2008: 200-206
5. Ahmadinejad S H, Jalili S, Abadi M. A hybrid model for correlating alerts of known and unknown attack scenarios and updating attack graphs. Computer Networks, 2011, 55(9): 2221-2240
6. Elshoush H T, Osman I M. Alert correlation in collaborative intelligent intrusion detection systems—a survey. Applied Soft Computing, 2011, 11(7): 4349-4365
7. Qi Y L, An H N. The evaluation model of network security based on fuzzy rough sets. Luo Q (ed). Advances in Wireless Networks and Information Systems. LNEE 72. Berlin, Germany: Springer, 2010: 517-525
8. Dong J F. The building of network security situation evaluation and prediction model based on grey theory. Proceedings of the 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE’10): Vol 2, Mar 6-7, 2010, Wuhan, China. Piscataway, NJ, USA: IEEE, 2010: 401-404
9. Man D P, Wang Y, Yang W, et al. A combined prediction method for network security situation. Proceedings of the 2010 International Conference on Computational Intelligence and Software Engineering (CiSE’10), Dec 10-12, 2010, Wuhan, China. Piscataway, NJ, USA: IEEE, 2010: 4p
10. Klein G, Günther H, Träber S. Modularizing cyber defense situational awareness-technical integration before human understanding. Aschenbruck N, Martini P, Meier M, et al (eds). Future Security. CCIS 318. Berlin, Germany: Springer, 2012: 307-310
11. Tang K, Zhou M T, Wang W Y. Insider cyber threat situational awareness framwork using dynamic Bayesian networks. Proceedings of the 4th International Conference on Computer Science and Education (ICCSE'09) , Jul 25-28, 2009, London, UK. Piscataway, NJ, USA: IEEE, 2009: 1146-1150
12. Liang Y, Wang H Q, Pang Y G. A kind of formal modelling for network security situational awareness based on HMM. Proceedings of the 9th International Conference on Web-Age Information Management (WAIM'08), Jul 20-22, 2008, Zhangjiajie, China. Piscataway, NJ, USA: IEEE, 2008: 598-605
13. Qu Z Y, Li Y Y, Li P. A network security situation evaluation method based on DS evidence theory. Proceedings of the 2nd International Conference on Environmental Science and Information Application Technology (ESIAT’10): Vol 2, Jul 17-18, 2010, Wuhan, China. Piscataway, NJ, USA: IEEE, 2010: 496-499
14. Chen X Z, Zheng Q H, Guan X H, et al. Quantitative hierarchical threat evaluation model for network security. Journal of Software, 2006, 17(4): 885-897 (in Chinese)
15. Zhang H B, Huang Q, Li F W, et al. A network security situation prediction model based on wavelet neural network with optimized parameters. Digital Communications and Networks, 2016, 2(3): 139-144
16. Chen J, Tu X G. Network security risk assessment based on support vector machine. Proceedings of the IEEE 3rd International Conference on Communication Software and Networks (ICCSN’11), May 27-29, 2011, Xi’an, China. Piscataway, NJ, USA: IEEE, 2011: 184-187
17. Bamakan S M H, Wang H D, Tian Y J, et al. An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing, 2016, 199: 90-102
18. Gao Y Y, Shen Y J, Zhang G D, et al. Information security risk assessment model based on optimized support vector machine with artificial fish swarm algorithm. Proceedings of the IEEE 6th International Conference on Software Engineering and Service Science (ICSESS’15), Sept 23-25, 2015, Beijing, China. Piscataway, NJ, USA: IEEE, 2015: 599-602
19. Zeng B, Zhong P. Simulation research on network security situation prediction method. Computer Simulation, 2012, 29 (5): 170-173 (in Chinese)
20. Deng J L. Grey system theory and course. Wuhan, China: Huazhong University of Science and Technology Press, 1990: 89-90 (in Chinese)
21. Wu C H, Ho J M, Lee D T. Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transportation Systems, 2004, 5(4): 276-281
22. Hu G Y, Zhou Z J, Zhang B C, et al. A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm. Applied Soft Computing, 2016, 48: 404-418
Lin S W, Lee Z J, Chen S C, et al. Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied Soft Computing, 2008, 8(4): 1505-1512 |
