Importance sampling for the simulation of highly reliable Markovian systems
Management Science
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A graph-based system for network-vulnerability analysis
Proceedings of the 1998 workshop on New security paradigms
Automated Generation and Analysis of Attack Graphs
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Scenario graphs and attack graphs
Scenario graphs and attack graphs
Model-Based Evaluation: From Dependability to Security
IEEE Transactions on Dependable and Secure Computing
Modeling and Simulation in Security Evaluation
IEEE Security and Privacy
Quantified security is a weak hypothesis: a critical survey of results and assumptions
NSPW '09 Proceedings of the 2009 workshop on New security paradigms workshop
MMM-ACNS'05 Proceedings of the Third international conference on Mathematical Methods, Models, and Architectures for Computer Network Security
Attack graph based evaluation of network security
CMS'06 Proceedings of the 10th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
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Traditional approaches to security evaluation have been based on penetration testing of real systems, or analysis of formal models of such systems. The former suffer from the problem that the security metrics are based on only a few of the possible paths through the system. The latter suffer from the inability to analyze detailed system descriptions due to the rapid explosion of state space sizes, which render the models intractable for tools such as model checkers. We propose an approach to obtain statistically valid estimates of security metrics by performing repeated penetration testing of detailed system models. We make use of importance sampling techniques to help reduce the variance of our estimates, and achieve relative error bounds quickly. We validate our approach by estimating security metrics of a large model with more than 21700 possible states.