Abstract
ICT systems are becoming increasingly complex and dynamic. They mostly include a large number of heterogeneous and interconnected assets (both physically and logically), which may be in turn exposed to multiple security flaws and vulnerabilities. Moreover, dynamicity is becoming paramount in modern ICT systems, since new assets and device configurations may be constantly added, updated, and removed from the system, leading to new security flaws that were not even existing at design time. From a risk assessment perspective, this adds new challenges to the defenders, as they are required to maintain risks within an acceptable range, while the system itself may be constantly evolving, sometimes in an unpredictable way. This paper introduces a new risk assessment framework that is aimed to address these specific challenges and that advances the state of the art along two distinct directions. First, we introduce the risk assessment graphs (RAGs), which provide a model and formalism that enable to characterize the system and its encountered risks. Nodes in the RAG represent each asset and its associated vulnerability, while edges represent the risk propagation between two adjacent nodes. Risk propagations in the graph are determined through two different metrics, namely the accessibility and potentiality, both formulated as a function of time and respectively capture the topology of the system and its risk exposure, as well as the way they evolve over time. Second, we introduce a quantitative risk assessment approach that leverages the RAGs in order to compute all possible attack paths in the system and to further infer their induced risks. Our approach achieves both flexibility and generality requirements and applies to a wide set of applications. In this paper, we demonstrate its usage in the context of a software-defined networking (SDN) testbed, and we conduct multiple experiments to evaluate the efficiency and scalability of our solution.
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References
Purdy G (2010) ISO 31000: 2009—setting a new standard for risk management. Risk Anal 30(6):881–886
EBIOS, Central directorate for information systems security, version, http://www.ssi.gouv.fr
Alberts C J, Behrens S G, Pethia R D, Wilson W R (1999) Operationally critical threat, asset, and vulnerability evaluation (OCTAVE) framework. Version 1.0
Mell P, Scarfone K, Romanosky S (2007) A complete guide to the common vulnerability scoring system version 2.0. Published by FIRST-forum of incident response and security teams, 1–23
Sheyner OM (2004) Scenario graphs and attack graphs (Doctoral dissertation, US Air Force Research Laboratory)
Bondy J A, Murty U S R (1976) Graph theory with applications, vol 290. London: Macmillan
West DB (2001) Introduction to graph theory, vol 2. Upper Saddle River: Prentice hall
NIST, National institute of science and technology, http://nvd.nist.gov/download.cfm
Phillips C, Swiler L P (1998) A graph-based system for network-vulnerability analysis. In: Proceedings of the 1998 workshop on new security paradigms, pp 71–79
Ou X, Boyer W F, McQueen M A (2006) A scalable approach to attack graph generation. In: Proceedings of the 13th ACM con- ference on computer and communications security, pp 336–345
Ammann P, Wijesekera D, Kaushik S (2002) Scalable, graph-based network vulnerability analysis. In: Proceedings of the 9th ACM conference on computer and communications security, pp 217–224
Huang H, Zhang S, Ou X, Prakash A, Sakallah K (2011) Distilling critical attack graph surface iteratively through minimum-cost sat solving. In: Proceedings of the 27th annual computer security applications conference, pp 31–40
Viduto V, Huang W, Maple C (2011) Toward optimal multi-objective models of network security: survey. In: Automation and computing, ICAC, pp 6–11
Xie P, Li J H, Ou X, Liu P, Levy R (2010) Using Bayesian networks for cyber security analysis. In: IEEE/IFIP international con- ference on dependable systems and networks, 2010, pp 211–220
Mehta V, Bartzis C, Zhu H, Clarke E, Wing J (2006) Ranking attack graphs. In: Recent advances in intrusion detection, pp 127–144
Kijsanayothin P, Hewett R (2010) Analytical approach to attack graph analysis for network security. In: ARES’10 international conference on availability, reliability, and security, pp 25–32
Wing J M et al. (2008) Scenario graphs applied to network security. In: Information assurance: survivability and security in networked systems, pp 247–277
Homer J, Zhang S, Ou X, Schmidt D, Du Y, Rajagopalan S R, Singhal A (2013) Aggregating vulnerability metrics in enterprise networks using attack graphs. J Comput Secur 21(4):561–597
Lippmann R P, Ingols KW (2005) An annotated review of past papers on attack graphs (No. PR-IA-1). Massachusetts Inst Of Tech Lexington Lincoln Lab
Hong J, Kim D -S (2012) HARMs: hierarchical attack representation models for network security analysis. Security Research Institute, Edith Cowan University, Perth, Western Australia
Wang S, Zhang Z, Kadobayashi Y (2013) Exploring attack graph for cost-benefit security hardening: a probabilistic approach. Comput Secur 32:158–169
Samarji L, Cuppens F, Cuppens-Boulahia N, Kanoun W, Dubus S (2013) Situation calculus and graph based defensive modeling of simultaneous attacks. In: Cyberspace safety and security, pp 132–150
Common vulnerabilities and exposures, CVE, http://cve.mitre.org/
Van Benthem J (2011) Logical dynamics of information and interaction. Cambridge University Press
Noel S, Jajodia S, O’Berry B, Jacobs M (2003) Efficient minimum-cost network hardening via exploit dependency graphs. In: 19th annual computer security applications conference pro- ceedings, pp 86–95
Jakobson G (2011) Mission cyber security situation assessment using impact dependency graphs. In: Proceedings of the 14th international conference on information fusion (FUSION), pp 1–8
Kheir N, Cuppens-Boulahia N, Cuppens F, Debar H (2010) A service dependency model for cost-sensitive intrusion response. In: Computer security–ESORICS, pp 626–642
Shandilya V, Simmons C B, Shiva S (2014) Use of attack graphs in security systems. Journal of Computer Networks and Communications, 2014
Yassine N M, Nancy P, Nizar K, Mahjoub A R, Wary J P (2016) A new risk assessment framework using graph theory for complex ICT systems. In: Proceedings of the 2016 international workshop on managing insider security threats. ACM, pp 97– 100
Baras J S, Theodorakopoulos G (2010) Path problems in networks. Synthesis Lectures on Communication Networks 3(1):1–77
Floyd R W (1962) Algorithm 97: shortest path. Commun ACM 5(6):345
Ahmad I, Namal S, Ylianttila M et al. (2015) Security in software defined networks: a survey. IEEE Commun Surv Tutorials 17(4):2317–2346
Common platform enumeration, CPE, https://cpe.mitre.org/
Networkx documentation, https://networkx.github.io/documentation/networkx-1.9.1/
Erdös P, Rényi A (1959) On random graphs, I. Publicationes Mathematicae (Debrecen) 6:290–297
Ben-Tal A, El Ghaoui L, Nemirovski A (2009) Robust optimization. Princeton University Press
Schrijver A (2002) Combinatorial optimization: polyhedra and efficiency, vol 24. Springer Science & Business Media
Dantzig GB (1998) Linear programming and extensions. Princeton University Press
IBM ILOG CPLEX Optimizer, http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/
Mahjoub A R, Naghmouchi M Y, Perrot N (2017) A bi-level programming model for proactive countermeasure selection in complex ICT systems, INOC. Lisbonne, Portugal
Acknowledgements
We would like to thank the anonymous referees for their valuable comments which permitted to improve the presentation of the paper.
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Appendices
Appendix A: Table of notations
In Table 2, we describe the different notations used in this paper.
Appendix B: Future work
Future work will expand our approach described in this paper through integrating a risk treatment step. A possible illustration of the entire process is provided in Fig. 10. The risk treatment process deals with the following Proactive Countermeasure Selection Problem (PCSP): Given the RAGs, the countermeasures and the security policies (thresholds), find an assignment of countermeasures to the asset-vulnerability nodes that both respects the security policies and minimizes the cost of its deployment. The solution of the problem may be conducted in two steps.
PCSP problem modeling
A mathematical programming formulation will be given to model the PCSP.
PCSP problem solving
Based on the formulation, efficient optimization algorithms will be developed to solve the problem. The solver Cplex [39] will be used.
A preliminary work related to this problem is published in [40].
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Kheir, N., Mahjoub, A.R., Naghmouchi, M.Y. et al. Assessing the risk of complex ICT systems. Ann. Telecommun. 73, 95–109 (2018). https://doi.org/10.1007/s12243-017-0617-0
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DOI: https://doi.org/10.1007/s12243-017-0617-0