Abstract
As known, security system administrators need to be aware of the security risks and abnormal behaviors in a network system. Given the exploitation probability value of each vulnerability, the cumulative probability of an attack path from an attacker to a target node can be quantified and calculated, namely as the K maximum probability attack paths for a target node. It is proposed in this paper a design to compute the K maximum probability attack paths for a given set of target nodes, where available vulnerability sets for each node in the system are built and assigned to different access flags during the computation process of attack paths, aimed at reducing the computation costs. Experimental results show that the proposed design can improve the performance on the computation of the K maximum probability attack paths for a given set of target nodes, promising and more efficient than existing algorithms to generate the attack paths.
















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References
Almohri, H., Yao, D., Watson, L., Ou, X.: Security optimization of dynamic networks with probabilistic graph modeling and linear programming. IEEE Trans. Depend. Secure 13, 474–487 (2016)
Zhang, W., Han, D., Li, K.C., Massetto, F.I.: Wireless sensor network intrusion detection system based on MK-ELM. In: Soft Computing, Springer, Berlin
Ammann, P., Wijesekera, D., Kaushik, S.: Scalable, graph-based network vulnerability analysis. In: Proceedings of the 9th ACM Conference on Computer and Communications Security, Washington, DC, USA, 18–22 Nov 2002, ACM, New York, NY, USA, pp. 217–224
Kaynar, K.: A taxonomy for attack graph generation and usage in network security. J. Inf. Secur. Appl. 29, 27–56 (2016)
Common Vulnerability Scoring System. http://www.first.org/cvss. Accessed on 1 Oct 2019
Li, K., Gu, N.J., Bi, K., Ji, H.Z.: Network security evaluation algorithm based on access level vectors. In: Proceedings of the 9th International Conference for Young Computer Scientists, Hunan, China, 18–21 November 2008, IEEE, Piscataway, NJ, USA, pp. 1538–1544
Bi, K., Han, D.Z., Wang, J.: K maximum probability attack paths dynamic generation algorithm. Comput. Sci. Inf. Syst. 13, 677–689 (2016)
Phillips, C., Swiler, L.P.: A graph-based system for network-vulnerability analysis. In: Proceedings of the 1998 workshop on New security paradigms, Charlottesville, Virginia, USA, 22–26 September 1998, ACM, New York, NY, USA, pp. 71–79
Sheyner, O., Haines, J., Jha, S.: Automated generation and analysis of attack graphs. In: Proceedings of the 2002 IEEE Symposium on Security and Privacy, Berkeley, California, USA, 12–15 May 2002, IEEE, Piscataway, NJ, USA, pp. 273–284
OU, X.M., Boyer, F.W., McQueen, A.: A scalable approach to attack graph generation. In: Proceedings of the 13th ACM Conference on Computer and Communications Security, Alexandria, Virginia, USA, 30 October 03, November 2006, ACM, New York, NY, USA, pp. 336–345
Ingols, K., Lippmann, R., Piwowarski, K.: Practical attack graph generation for network defense. In: Proceedings of the 22nd Annual Computer security Applications Conference, Miami Beach, Florida, USA, 11–15 December 2006, IEEE, Piscataway, NJ, USA, pp. 121–130
Lippmann, R., Ingols, K., Scott, C., Piwowarski, K., Kratkiewicz, K., Artz, M., Cunningham, R.: Validating and restoring defense in depth using attack graphs. In: Proceedings of Military Communications Conference, Washington DC, USA, 23–25 October 2006, IEEE, Piscataway, NJ, USA, pp. 1–10
Chen, F., Su, J.S., Zhang, Y.: A scalable approach to full attack graphs generation. In: Engineering Secure Software and Systems, Lecture Notes in Computer Science, 2009, vol. 5429; Springer Berlin Heidelberg: Berlin, Heidelberg; pp. 150–163
Kent, A.D., Liebrock, L.M., Neil, J.C.: Authentication graphs: Analyzing user behavior within an enterprise network. Comput. Secur. 48, 150–166 (2015)
Shameli-Sendi, A., Cheriet, M., Hamou-Lhadj, A.: Taxonomy of intrusion risk assessment and response system. Comput. Secur. 45, 1–16 (2014)
Kaynar, K., Sivrikaya, F.: Distributed attack graph generation. IEEE Trans. Depend. Secure. 13, 519–532 (2016)
Chung, C.J., Khatkar, P., Xing, T., Lee, J., Huang, D.: Nice: Network intrusion detection and countermeasure selection in virtual network systems. IEEE Trans. Depend. Secure. 10, 198–211 (2013)
Bopche, G.S., Mehtre, B.M.: Graph similarity metrics for assessing temporal changes in attack surface of dynamic networks. Comput. Secur. 64, 16–43 (2017)
Poolsappasit, N., Dewri, R., Ray, I.: Dynamic security risk management using bayesian attack graphs. IEEE Trans. Depend. Secure. 9, 61–74 (2012)
Wang, S., Zhang, Z., Kadobayashi, Y.: Exploring attack graph for cost-benefit security hardening: A probabilistic approach. Comput. Secur. 32, 158–169 (2013)
Liang, W., Li, K.C., Long, J., Kui, X., Zomaya, A.Y.: An industrial network intrusion detection algorithm based on multifeature data clustering optimization model. IEEE Trans. Ind. Inform. 16(3), 2063–2071 (2019)
Liang, W., Tang, M., Long, J., Peng, X., Xu, J., Li, K.C.: A secure fabric blockchain-based data transmission technique for industrial Internet-of-Things. IEEE Trans. Ind. Inform. 15(6), 3582–3592 (2019)
W. Liang, J. Long, T.-H. Weng, X. Chen, K.-C. Li, A.Y. Zomaya, “TBRS: A trust based recommendation scheme for vehicular CPS network”, Future Generation Computer Systems, 92, p. 383-398, 2019, North-Holland [CrossRef]
Sherry, J., Hasan, S., Scott, C., Krishnamurthy, A., Ratnasamy, S., Sekar, V.: Making middleboxes someone else’s problem: network processing as a cloud service. ACM SIGCOMM Comput. Commun. Rev. 42(4), 13–24 (2012)
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Authors of this manuscript are grateful to the valuable comments provided by external reviewers and international experts for the improvement of technical and organization sections.
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This research was funded in part by the National Natural Science Foundation of China, Grant Numbers: 61672338, 51779136, 61873160 and 61373028.
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Bi, K., Han, D., Zhang, G. et al. K maximum probability attack paths generation algorithm for target nodes in networked systems. Int. J. Inf. Secur. 20, 535–551 (2021). https://doi.org/10.1007/s10207-020-00517-4
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DOI: https://doi.org/10.1007/s10207-020-00517-4