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A Novel Approach to Network Security Situation Assessment Based on Attack Confidence

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Network and System Security (NSS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10394))

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Abstract

As an active topic in the research field, network security situation assessment can reflect the security situation from a global perspective. However, existing assessment approaches rely on detection threshold to make decisions, leading to massive false positives and false negatives. This paper proposes a confidence-based network security situation assessment approach that preserves the probability information in attack detection. We use the ensemble learning algorithm and D-S evidence theory to obtain the attack confidence, and calculate the network security situation value through the situation elements fusion. Experiment results demonstrate that this approach is effective and accurate.

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References

  1. Bass, T.: Multisensor data fusion for next generation distributed intrusion detection systems (1999)

    Google Scholar 

  2. Bass, T.: Intrusion detection systems and multisensor data fusion. Commun. ACM 43(4), 99–105 (2000)

    Article  Google Scholar 

  3. Yong, W., Yifeng, L., Dengguo, F.: A network security situational awareness model based on information fusion. J. Comput. Res. Dev. 3 (2009)

    Google Scholar 

  4. Yong, Z., Xiaobin, T., Hongsheng, X.: A novel approach to network security situation awareness based on multi-perspective analysis. In: 2007 International Conference on Computational Intelligence and Security, pp. 768–772. IEEE (2007)

    Google Scholar 

  5. Liu, Y.L., Feng, G.D., Lian, Y.F.: Network situation prediction method based on spatial-time dimension analysis. J. Comput. Res. 51(8), 1681–1694 (2014)

    Google Scholar 

  6. Kokkonen, T., Hautamki, J., Siltanen, J., et al.: Model for sharing the information of cyber security situation awareness between organizations. In: 2016 23rd International Conference on Telecommunications (ICT), pp. 1–5. IEEE (2016)

    Google Scholar 

  7. Kabiri, P., Ghorbani, A.A.: Research on intrusion detection and response: a survey. IJ Netw. Secur. 1(2), 84–102 (2005)

    Google Scholar 

  8. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  9. Mukkamala, S., Janoski, G., Sung, A.: Intrusion detection using neural networks and support vector machines. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002, vol. 2, pp. 1702–1707. IEEE (2002)

    Google Scholar 

  10. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2013)

    MATH  Google Scholar 

  11. Platt J C. Probabilities for SV Machines. In: Advances in Large Margin Classifiers, pp. 61–74 (2008)

    Google Scholar 

  12. Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)

    MATH  Google Scholar 

  13. Syarif, I., Zaluska, E., Prugel-Bennett, A., Wills, G.: Application of bagging, boosting and stacking to intrusion detection. In: Perner, P. (ed.) MLDM 2012. LNCS, vol. 7376, pp. 593–602. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31537-4_46

    Chapter  Google Scholar 

  14. Ting, K.M., Witten, I.H.: Issues in stacked generalization. J. Artif. Intell. Res. (JAIR) 10, 271–289 (1999)

    MATH  Google Scholar 

  15. Krogh, A., Vedelsby, J.: Neural network ensembles, cross validation, and active learning. In: Advances in Neural Information Processing Systems, vol. 7, pp. 231–238 (1995)

    Google Scholar 

  16. Qu, Z.Y., Li, Y.Y., Li, P.: A network security situation evaluation method based on D-S evidence theory. In: 2010 International Conference on Environmental Science and Information Application Technology (ESIAT), pp. 496–499 (2010)

    Google Scholar 

  17. Common Vulnerability Scoring System v3.0: Specification Document. https://www.first.org/cvss/specification-document

  18. 1999 DARPA Intrusion Detection Evaluation Data Set. http://www.ll.mit.edu/ideval/data/1999data.html

  19. Lee, W., Stolfo, S.J., Mok, K.W.: A data mining framework for building intrusion detection models. In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 120–132. IEEE (1999)

    Google Scholar 

  20. Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539–550 (2009)

    Article  Google Scholar 

  21. Truth used in the detection scoring phase of the 1999 DARPA Intrusion Detection Evaluation. http://www.ll.mit.edu/ideval/files/master-listfile-condensed.txt

Download references

Acknowledgments

This work is supported by The National Natural Science Foundation of China (No. 61572460, No. 61272481), National Key R&D Program of China (No. 2016YFB0800703), The Open Project Program of the State Key Laboratory of Information Security (No. 2017-ZD-01), The National Information Security Special Projects of National Development, the Reform Commission of China [No. (2012)1424], China 111 Project (No. B16037). Open Project Program of the State Key Laboratory of Information Security (2016-MS-02).

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Correspondence to Yuqing Zhang .

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Liu, D. et al. (2017). A Novel Approach to Network Security Situation Assessment Based on Attack Confidence. In: Yan, Z., Molva, R., Mazurczyk, W., Kantola, R. (eds) Network and System Security. NSS 2017. Lecture Notes in Computer Science(), vol 10394. Springer, Cham. https://doi.org/10.1007/978-3-319-64701-2_33

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  • DOI: https://doi.org/10.1007/978-3-319-64701-2_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64700-5

  • Online ISBN: 978-3-319-64701-2

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