Abstract
Network security situation awareness is a new type of network security technology. It evaluates the network security situation in real time from a macro perspective. Also it can predict the trend of the development of the network security situation, providing a basis for the decision analysis of administrators. It is difficult to obtain complete and accurate information in network security situation assessment by using evidential network. So we introduce an evidential network based on Bayesian network to solve that problem. Firstly, transform the parent node information and inference rules into plausibility function so as to be compatible with imperfect and inaccurate information. Secondly, we use the full probability formula of Bayesian network as reference to make similar reasoning under the framework of evidence theory. Then transform the inference result to BPA form by using the minimum specificity algorithm, and obtain the final result by projection. Finally, an example of network security situation assessment is given to illustrate the rationality and effectiveness of the method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Li, Z., Goyal, A., Chen, Y., et al.: Towards situational awareness of large-scale botnet probing events. IEEE Trans. Inf. Foren. Secur. 6(1), 175–188 (2011)
Gong, Z., Zhuo, Y.: Research on network situational awareness. J. Softw. 7, 131–145 (2010)
Bass, T.: Intrusion detection systems and multisensor data fusion: creating cyberspace situational awareness. Commun. ACM 43(4), 99–105 (2000)
Wen, Z., Cao, C., Zhou, H.: Network security situation assessment method based on naive Bayesian classifier. Comput. Appl. 35(8), 2164–2168 (2015)
Ye, L., Tan, Z.: A network security situation assessment method based on deep learning. Intell. Comput. Appl. 9(06), 73–75+82 (2019)
Vinayakumar, R., Poornachandran, P., Soman, K.P.: Scalable framework for cyber threat situational awareness based on domain name systems data analysis. In: Roy, S.S., Samui, P., Deo, R., Ntalampiras, S. (eds.) Big Data in Engineering Applications. SBD, vol. 44, pp. 113–142. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8476-8_6
Chen, X., Zheng, Q., Guan, X., et al.: Quantitative hierarchy threat evaluation model for network security. J. Softw. 17(4), 885–897 (2006)
Liu, H., Liu, J., Hui, X.: Network security situation assessment based on cloud model and Markov Chain. Comput. Dig. Eng. 47(6), 1432–1436 (2019)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Yakowitz, J.: An introduction to Bayesian Networks. Technometrics 39(3), 336–337 (1997)
Terent’Yev, A.N., Bidyuk, P.I.: method of probabilistic inference from learning data in Bayesian networks. Cybern. Syst. Anal. 43(3), 391–396 (2007)
Simon, C., Weber, P., Evsukoff, A.: Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis. Reliab. Eng. Syst. Saf. 93(7), 950–963 (2008)
Appriou, A.: Uncertainty theories and multisensor data fusion. In: ISTE (2014)
Deng, X., Jiang, W.: Dependence assessment in human reliability analysis using an evidential network approach extended by belief rules and uncertainty measures. Ann. Nucl. Energy 117, 183–193 (2018)
Cheng, S., Niu, Y., Li, J., Tong, K., et al.: A method of network security situation assessment based on evidential reasoning rules. Comput. Dig. Eng. 46(8), 1603–1607 (2018)
Acknowledgment
The work is partially supported by National Natural Science Foundation of China (61703338, 61671384), Equipment Pre-Research Fund (61400010109).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Deng, X., Jiang, W. (2020). A Novel Method of Network Security Situation Assessment Based on Evidential Network. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12486. Springer, Cham. https://doi.org/10.1007/978-3-030-62223-7_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-62223-7_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62222-0
Online ISBN: 978-3-030-62223-7
eBook Packages: Computer ScienceComputer Science (R0)