Abstract
Network security analysis based on attack graphs has been applied extensively in recent years. The ranking of nodes in an attack graph is an important step towards analyzing network security. This paper proposes an alternative attack graph ranking scheme based on a recent approach to machine learning in a structured graph domain, namely, Graph Neural Networks (GNNs). Evidence is presented in this paper that the GNN is suitable for the task of ranking attack graphs by learning a ranking function from examples and generalizes the function to unseen possibly noisy data, thus showing that the GNN provides an effective alternative ranking method for attack graphs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bhadeshia, University Of Cambridge. Neural Networks in Materials Science
Mizutani, E., Dreyfus, S.E.: On complexity analysis of supervised MLP-learning for algorithmic comparisons. In: International Joint Conference on Neural Networks (IJCNN), vol. 1 (2001)
Scarselli, F., Tsoi, A.C., Gori, M., Hagenbuchner, M.: A new neural network model for graph processing. Technical Report DII 1/05, University of Siena (August 2005)
Scarselli, F., Yong, S.L., Hagenbuchner, M., Tsoi, A.C.: Adaptive Page Ranking with Neural Networks. In: WWW (Special interest tracks and posters), pp. 936–937 (2005)
Frasconi, P., Gori, M., Sperduti, A.: A general framework for adaptive processing of data structures 9(5), 768–786 (September 1998)
Haykin, S.: Neural Networks, A Comprehensive Foundation. Macmillan College Publishing Company, Inc., 866 Third Avenue, New York, 10022 (1994)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366 (1989)
Kemmerer, R.A., Catherine, M., Millen, J.K.: Three system for cryptographic protocol analysis. Cryptology 7(2), 79–130 (1994)
Lu, L., Safavi-Naini, R., Horton, J., Susilo, W.: An adversary aware and intrusion detection aware attack model ranking scheme. In: Katz, J., Yung, M. (eds.) ACNS 2007. LNCS, vol. 4521, pp. 65–86. Springer, Heidelberg (2007)
Bianchini, M., Gori, M., Scarselli, F.: Inside PageRank. ACM Transactions on Internet Technology 5(1), 92–118 (2001)
Sheyner, O., Haines, J., Jha, S., Lippmann, R., Wing, J.: Automated generation and analysis of attack graphs. In: Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA (May 2002)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: Computational capabilities of graph neural networks. IEEE Transactions on Neural Networks 20(1), 81–102 (2009)
Scarselli, F., Yong, S.L., Gori, M., Hagenbuchner, M., Tsoi, A.C., Maggini, M.: Graph neural networks for ranking web pages. In: Web Intelligence Conference, pp. 666–672 (2005)
Tsoi, A.C., Hagenbuchner, M., Scarselli, F.: Computing customized page ranks. ACM Transactions on Internet Technology 6(4), 381–414 (2006)
Mehta, V., Bartzis, C., Zhu, H., Clarke, E., Wing, J.M.: Ranking attack graphs. In: Zamboni, D., Krügel, C. (eds.) RAID 2006. LNCS, vol. 4219, pp. 127–144. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, L. et al. (2009). Ranking Attack Graphs with Graph Neural Networks. In: Bao, F., Li, H., Wang, G. (eds) Information Security Practice and Experience. ISPEC 2009. Lecture Notes in Computer Science, vol 5451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00843-6_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-00843-6_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00842-9
Online ISBN: 978-3-642-00843-6
eBook Packages: Computer ScienceComputer Science (R0)