Abstract
| Original language | English |
|---|---|
| Title of host publication | Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance |
| Subtitle of host publication | 9th International Workshop, DPM 2014, 7th International Workshop, SETOP 2014, and 3rd International Workshop, QASA 2014, Wroclaw, Poland, September 10-11, 2014. Revised Selected Papers |
| Editors | Joaquin Garcia-Alfaro, Jordi Herrera-Joancomartí, Emil Lupu, Joachim Posegga |
| Place of Publication | Berlin |
| Publisher | Springer |
| Pages | 201-215 |
| Number of pages | 15 |
| ISBN (Electronic) | 978-3-319-17016-9 |
| ISBN (Print) | 978-3-319-17015-2 |
| DOIs | |
| Publication status | Published - 28 Mar 2015 |
| Event | 3rd International Workshop on Quantitative Aspects in Security Assurance, QASA 2014 - Wraclaw, Poland Duration: 10 Sep 2014 → 11 Sep 2014 Conference number: 3 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 8872 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Workshop
| Workshop | 3rd International Workshop on Quantitative Aspects in Security Assurance, QASA 2014 |
|---|---|
| Abbreviated title | QASA |
| Country | Poland |
| City | Wraclaw |
| Period | 10/09/14 → 11/09/14 |
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Keywords
- EC Grant Agreement nr.: FP7/318003
- SCS-cybersecurity
- EC Grant Agreement nr.: FP7/2007-2013
- Control strength
- Security metrics
- Adversarial risk analysis
- Attack trees
- Attacker models
- Fitness functions
- Simulation
Cite this
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Calculating Adversarial Risk from Attack Trees : Control Strength and Probabilistic Attackers. / Pieters, Wolter; Davarynejad, Mohsen.
Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance: 9th International Workshop, DPM 2014, 7th International Workshop, SETOP 2014, and 3rd International Workshop, QASA 2014, Wroclaw, Poland, September 10-11, 2014. Revised Selected Papers. ed. / Joaquin Garcia-Alfaro; Jordi Herrera-Joancomartí; Emil Lupu; Joachim Posegga. Berlin : Springer, 2015. p. 201-215 (Lecture Notes in Computer Science; Vol. 8872).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Calculating Adversarial Risk from Attack Trees
T2 - Control Strength and Probabilistic Attackers
AU - Pieters, Wolter
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
Y1 - 2015/3/28
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.
KW - EC Grant Agreement nr.: FP7/318003
KW - SCS-cybersecurity
KW - EC Grant Agreement nr.: FP7/2007-2013
KW - Control strength
KW - Security metrics
KW - Adversarial risk analysis
KW - Attack trees
KW - Attacker models
KW - Fitness functions
KW - Simulation
U2 - 10.1007/978-3-319-17016-9_13
DO - 10.1007/978-3-319-17016-9_13
M3 - Conference contribution
SN - 978-3-319-17015-2
T3 - Lecture Notes in Computer Science
SP - 201
EP - 215
BT - Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance
A2 - Garcia-Alfaro, Joaquin
A2 - Herrera-Joancomartí, Jordi
A2 - Lupu, Emil
A2 - Posegga, Joachim
PB - Springer
CY - Berlin
ER -