Branching programs and binary decision diagrams: theory and applications
Branching programs and binary decision diagrams: theory and applications
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Scalable, graph-based network vulnerability analysis
Proceedings of the 9th ACM conference on Computer and communications security
A framework for analyzing and mitigating the vulnerabilities of complex systems via attack and protection trees
Computing Exact Outcomes of Multi-parameter Attack Trees
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part II on On the Move to Meaningful Internet Systems
Processing multi-parameter attacktrees with estimated parameter values
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
Practical security analysis of e-voting systems
IWSEC'07 Proceedings of the Security 2nd international conference on Advances in information and computer security
Serial model for attack tree computations
ICISC'09 Proceedings of the 12th international conference on Information security and cryptology
Rational choice of security measures via multi-parameter attack trees
CRITIS'06 Proceedings of the First international conference on Critical Information Infrastructures Security
ICISC'05 Proceedings of the 8th international conference on Information Security and Cryptology
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Attack tree analysis is used to estimate different parameters of general security threats based on information available for atomic sub-threats. We focus on estimating the expected gains of an adversary based on both the cost and likelihood of the subthreats. Such a multi-parameter analysis is considerably more complicated than separate probability or skill level estimation, requiring exponential time in general. However, this paper shows that under reasonable assumptions a completely different type of optimal substructure exists which can be harnessed into a linear-time algorithm for optimal gains estimation. More concretely, we use a decision-theoretic framework in which a rational adversary sequentially considers and performs the available attacks. The assumption of rationality serves as an upper bound as any irrational behavior will just hurt the end result of the adversary himself. We show that if the attacker considers the attacks in a goal-oriented way, his optimal expected gains can be computed in linear time. Our model places the least restrictions on adversarial behavior of all known attack tree models that analyze economic viability of an attack and, as such, provides for the best efficiently computable estimate for the potential reward.