MultiGraph

Software component acting as a simulator and aiming to help in the deployment of novel Attack Graph (AG) models. It is intended to help comparing these novel approaches with already existing designs and implementations. It has also as an objective to determine those aspects of existing models that have not been completely defined or specified by their authors and thus may need some completion before being used in lab or real attack scenarios.

Features

• AG import from MulVAL
• Optimization problem solutions:
  • Poolsappasit et al.
  • Wang L. et al.
  • Almohri et al.
• Interactive graph (i.e. modify AGs w/ GUI)
• Export CSV lists with "prescriptions"
• Comparisons of different methods and strategies - TODO

GUI

Bayesian AG with 3 vulnerabilities

Input XML file generated by MulVAL

AG with one vulnerability

Output of prescriptions

(Poolsappasit et al. case)

cmId,targetId,type
pn15,n15,SI_02: FLAW REMEDIATION

(L. Wang et al. case)

nodeIds
!n4 | (!n3 & !n8) | (!n3 & !n9) | (!n5 & !n8) | (!n5 & !n9)

(H. Almohri et al. case)

nodeIds // in order of effectiveness
74,75
74
75
[]

References

• M. Zago, Modeling Cyber-Threats: Adopting Bayes' principles in the Attack Graph theory, 2015
• Poolsappasit, N., Dewri, R., and Ray, I. Dynamic security risk management using bayesian attack graphs. IEEE Transactions on Dependable and Secure Computing 9, 1 (Jan 2012), 61–74.
• Wang L., Albanese M., Jajodia S., Network Hardening An Automated Approach to Improving Network Security, Springer International Publishing, Cham 2014, pp. 15-22
• H. M. J. Almohri, L. T. Watson, D. Yao and X. Ou, "Security Optimization of Dynamic Networks with Probabilistic Graph Modeling and Linear Programming," in IEEE Trans. on Dep. & Secure Computing, vol. 13, no. 4, pp. 474-487, 1 July-Aug. 2016.