Abstract
The network infrastructure is the most critical technical asset of any organization. This network architecture must be useful, efficient, and secure. However, their cybersecurity challenges are immense as the number of attacks is increasing. Consequently, there is a need to have efficient tools to assess the risks, know the vulnerabilities, and find the solutions before the attackers exploit them. The challenges remain in integrating the vulnerability analysis tools in a holistic process that cyber defenders can use to detect an intrusion and respond quickly. Attack graphs showed great importance in analyzing security. In this paper, we present a survey of raised and related topics to the field of vulnerability analysis and attack graphs.
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References
Filkins, B.: Network Security Infrastructure and Best Practices: A SANS Survey. SANS Institute, Washington (2017)
Identity Theft Resource Center: ITRC Data Breach Report (2017)
Smith, S.S.: Internet Crime Report 2016, 29920 (2016)
Hall, T., Hau, B., Penrose, M., Bevilacqua, M.: Mandiant M-Trends 2016 EMEA Edition, pp. 1–18 (2016)
Liu, Z., Li, S., He, J., Xie, D., Deng, Z.: Complex network security analysis based on attack graph model. In: 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 183–186 (2012)
Sheyner, O., Wing, J.: Tools for generating and analyzing attack graphs. In: 2nd International Symposium on Formal Methods for Components and Objects (FMCO’03), vol. 3188, pp. 344–371 (2004)
Abraham, S., Nair, S.: A Predictive Framework for Cyber Security Analytics Using Attack Graphs, pp. 1–17 (2015)
Pirolli, P., Russell, D.M.: Introduction to this special issue on sensemaking. Hum.-Comput. Interact. 26, 1–8 (2011)
Seuwou, P., Banissi, E., Ubakanma, G., Sharif, M.S., Healey, A.: Actor-network theory as a framework to analyse technology acceptance model’s external variables: the case of autonomous vehicles. Commun. Comput. Inf. Sci. 630, 305–320 (2016)
Singhal, A., Ou, X.: Security Risk Analysis of Enterprise Networks Using Probabilistic Attack Graphs, pp. 1–22 (2011). https://doi.org/10.6028/nist.ir.7788
Stevens-Adams, S., Carbajal, A., Silva, A., Nauer, K., Anderson, B., Reed, T., Forsythe, C.: Enhanced Training for Cyber Situational Awareness. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (LNAI), vol. 8027, pp. 90–99 (2013)
Jajodia, S., Peng, L., Swarup, V., Wang, C.: Cyber Situational Awareness Testing, vol. 2016, pp. 209–233. Springer (2016)
Wang, C., Du, N., Yang, H.: Generation and analysis of attack graphs. Procedia Eng. 29, 4053–4057 (2012)
Wang, X., Liao, Y.: A replication detection scheme for sensor networks. Procedia Eng. 29, 21–26 (2012)
Yang, J., Liang, L., Yang, Y. and Zhu, G.: A hierarchical network security risk assessment method based on vulnerability attack link generated. In: 2012 4th International Symposium on Information Science and Engineering (ISISE 2012), vol. 1, pp. 113–118 (2012)
Ye, Y., Xu, X.S., Qi, Z.C.: A probabilistic computing approach of attack graph-based nodes in large-scale network. Procedia Environ. Sci. 10, 3–8 (2011)
Pino, R.E.: Cybersecurity Systems for Human Cognition Augmentation. Springer, New York (2014)
Homer, J., Ou, X., Schmidt, D.: A sound and practical approach to quantifying security risk in enterprise networks. Technical Report, pp. 1–15. Kansas State University (2009)
Hamid, R.S., Yasser, G., Rasool, J.: Topological analysis of multi-phase attacks using expert systems. J. Inf. Sci. Eng. 767, 743–767 (2008)
Noel, S., Jajodia, S.: Understanding complex network attack graphs through clustered adjacency matrices. Proceedings-Annual Computer Security Applications Conference (ACSAC) 2005, 160–169 (2005)
Lippmann, R.P., Ingols, K.W.: An annotated review of past papers on attack graphs. No. PR-IA-1 (2005)
Artz, M.L.: NetSPA: a network security planning architecture. Netw. Secur. 2001, 1–97 (2002)
Ou, X., Govindavajhala, S., Appel, A.W.: MulVAL: a logic-based network security analyzer. In: Proceedings of the 14th conference on USENIX Security Symposium, vol. 14 (2005)
Oltsik, J.: Integrated Network Security Architecture: Threat-Focused Next-generation Firewall. The Enterprise Strategy Group, Inc. (2014)
Mell, P., Harang, R.: Minimizing attack graph data structures. In: ICSEA 2015: Tenth International Conference on Software Engineering Advances. Barcelona (2015)
Bacic, E., Froh, M., Henderson, G.: Mulval extensions for dynamic asset protection (2006)
Frigault, M., Wang, L.: Measuring network security using bayesian network-based attack graphs. In: Proceedings—International Computer Software and Applications Conference, pp. 698–703 (2008)
Kaynar, K.: A taxonomy for attack graph generation and usage in network security. J. Inf. Secur. Appl. 29, 27–56 (2016)
Long, X., Wu, X.: Motion segmentation based on edge detection. Procedia Eng. 29, 74–78 (2012)
Ma, J.C., Wang, Y.J., Sun, J.Y., Chen, S.: A minimum cost of network hardening model based on attack graphs. Procedia Eng. 15, 3227–3233 (2011)
Mourad, A., Soeanu, A., Laverdière, M.A., Debbabi, M.: New aspect-oriented constructs for security hardening concerns. Comput. Secur. 28, 341–358 (2009)
Ou, X., Govindavajhala, S., Appel, A.W: Policy-based multihost multistage vulnerability analysis (2005)
Dimitrios, P., Sarandis, M., Christos, D.: Expanding topological vulnerability analysis to intrusion detection through the incident response intelligence system. Inf. Manage. Comput. Secur. 4 (2010)
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Ait Maalem Lahcen, R., Mohapatra, R., Kumar, M. (2018). Cybersecurity: A Survey of Vulnerability Analysis and Attack Graphs. In: Ghosh, D., Giri, D., Mohapatra, R., Sakurai, K., Savas, E., Som, T. (eds) Mathematics and Computing. ICMC 2018. Springer Proceedings in Mathematics & Statistics, vol 253. Springer, Singapore. https://doi.org/10.1007/978-981-13-2095-8_9
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