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A Novel Method of Network Security Situation Assessment Based on Evidential Network

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Machine Learning for Cyber Security (ML4CS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12486))

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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.

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Acknowledgment

The work is partially supported by National Natural Science Foundation of China (61703338, 61671384), Equipment Pre-Research Fund (61400010109).

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Correspondence to Xinyang Deng .

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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

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  • DOI: https://doi.org/10.1007/978-3-030-62223-7_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62222-0

  • Online ISBN: 978-3-030-62223-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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