Abstract
Attack trees are a well-known formalism for quantitative analysis of cyber attacks consisting of multiple steps and alternative paths. It is possible to derive properties of the overall attacks from properties of individual steps, such as cost for the attacker and probability of success. However, in existing formalisms, such properties are considered independent. For example, investing more in an attack step would not increase the probability of success. As this seems counterintuitive, we introduce a framework for reasoning about attack trees based on the notion of control strength, annotating nodes with a function from attacker investment to probability of success. Calculation rules on such trees are defined to enable analysis of optimal attacker investment. Our second result consists of the translation of optimal attacker investment into the associated adversarial risk, yielding what we call adversarial risk trees. The third result is the introduction of probabilistic attacker strategies, based on the fitness (utility) of available scenarios. Together these contributions improve the possibilities for using attack trees in adversarial risk analysis.
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Notes
- 1.
Note that picking argmax may involve nondeterministic choice if multiple arguments produce the maximum. This is one of the issues that the probabilistic attackers in this paper (Sect. 5) help to solve.
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Acknowledgements
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement number ICT-318003 (TREsPASS). This publication reflects only the authors’ views and the Union is not liable for any use that may be made of the information contained herein.
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Pieters, W., Davarynejad, M. (2015). Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers. In: Garcia-Alfaro, J., et al. Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance. DPM QASA SETOP 2014 2014 2014. Lecture Notes in Computer Science(), vol 8872. Springer, Cham. https://doi.org/10.1007/978-3-319-17016-9_13
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DOI: https://doi.org/10.1007/978-3-319-17016-9_13
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