Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers

Wolter Pieters, Mohsen Davarynejad

  • 5 Citations

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 strate- gies, based on the fitness (utility) of available scenarios. Together these contributions improve the possibilities for using attack trees in adversarial risk analysis.
Original languageUndefined
Title of host publication9th International Workshop on Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (DPM)
Place of PublicationBerlin
PublisherSpringer
Pages201-215
Number of pages15
ISBN (Print)978-3-319-17015-2
DOIs
StatePublished - 28 Mar 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8872
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Trees (mathematics)
Risk analysis
Chemical analysis
Costs

Keywords

  • EC Grant Agreement nr.: FP7/318003
  • EWI-26352
  • SCS-Cybersecurity
  • EC Grant Agreement nr.: FP7/2007-2013
  • Control strength
  • Security Metrics
  • METIS-312733
  • IR-97593
  • Adversarial risk analysis
  • Attack trees
  • Attacker models
  • Fitness functions
  • Simulation

Cite this

Pieters, W., & Davarynejad, M. (2015). Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers. In 9th International Workshop on Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (DPM) (pp. 201-215). (Lecture Notes in Computer Science; Vol. 8872). Berlin: Springer. DOI: 10.1007/978-3-319-17016-9_13

Pieters, Wolter; Davarynejad, Mohsen / Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers.

9th International Workshop on Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (DPM). Berlin : Springer, 2015. p. 201-215 (Lecture Notes in Computer Science; Vol. 8872).

Research output: Scientific - peer-reviewConference contribution

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Pieters, W & Davarynejad, M 2015, Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers. in 9th International Workshop on Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (DPM). Lecture Notes in Computer Science, vol. 8872, Springer, Berlin, pp. 201-215. DOI: 10.1007/978-3-319-17016-9_13

Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers. / Pieters, Wolter; Davarynejad, Mohsen.

9th International Workshop on Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (DPM). Berlin : Springer, 2015. p. 201-215 (Lecture Notes in Computer Science; Vol. 8872).

Research output: Scientific - peer-reviewConference contribution

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AU - Davarynejad,Mohsen

N1 - Foreground = 100%; Type of activity = Conference; Main leader = TUD; Type of audience = Scientific community; Size of audience = 20; Countries addressed = International;

PY - 2015/3/28

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N2 - 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 strate- gies, based on the fitness (utility) of available scenarios. Together these contributions improve the possibilities for using attack trees in adversarial risk analysis.

AB - 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 strate- gies, 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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Pieters W, Davarynejad M. Calculating Adversarial Risk from Attack Trees: Control Strength and Probabilistic Attackers. In 9th International Workshop on Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (DPM). Berlin: Springer. 2015. p. 201-215. (Lecture Notes in Computer Science). Available from, DOI: 10.1007/978-3-319-17016-9_13