Learning Attack Trees by Genetic Algorithms

D 2023

Learning Attack Trees by Genetic Algorithms

DORFHUBER, Florian Sebastian, Julia EISENTRAUT a Jan KŘETÍNSKÝ

Základní údaje

Originální název

Learning Attack Trees by Genetic Algorithms

Autoři

DORFHUBER, Florian Sebastian (276 Německo, domácí), Julia EISENTRAUT (276 Německo) a Jan KŘETÍNSKÝ (203 Česká republika, domácí)

Vydání

Lima, Theoretical Aspects of Computing – ICTAC 2023, od s. 55-73, 19 s. 2023

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/23:00133581

Organizační jednotka

Fakulta informatiky

ISBN

978-3-031-47962-5

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-47963-2_5

UT WoS

001160556100005

Klíčová slova anglicky

genetic algorithms

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 8. 4. 2024 10:13, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

Attack trees are a graphical formalism for security assessment. They are particularly valued for their explainability and high accessibility without security or formal methods expertise. They can be used, for instance, to quantify the global insecurity of a system arising from the unreliability of its parts, graphically explain security bottlenecks, or identify additional vulnerabilities through their systematic decomposition. However, in most cases, the main hindrance in the practical deployment is the need for a domain expert to construct the tree manually or using further models. This paper demonstrates how to learn attack trees from logs, i.e., sets of traces, typically stored abundantly in many application domains. To this end, we design a genetic algorithm and apply it to classes of trees with different expressive power. Our experiments on real data show that comparably simple yet highly accurate trees can be learned efficiently, even from small data sets.

Citovat

DORFHUBER, Florian Sebastian, Julia EISENTRAUT a Jan KŘETÍNSKÝ. Learning Attack Trees by Genetic Algorithms. Online. In Theoretical Aspects of Computing – ICTAC 2023. Lima: Springer, 2023, s. 55-73. ISBN 978-3-031-47962-5. Dostupné z: https://dx.doi.org/10.1007/978-3-031-47963-2_5.

@inproceedings{2375977,
   author = {Dorfhuber, Florian Sebastian and Eisentraut, Julia and Křetínský, Jan},
   address = {Lima},
   booktitle = {Theoretical Aspects of Computing – ICTAC 2023},
   doi = {http://dx.doi.org/10.1007/978-3-031-47963-2_5},
   keywords = {genetic algorithms},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Lima},
   isbn = {978-3-031-47962-5},
   pages = {55-73},
   publisher = {Springer},
   title = {Learning Attack Trees by Genetic Algorithms},
   year = {2023}
}

TY  - JOUR
ID  - 2375977
AU  - Dorfhuber, Florian Sebastian - Eisentraut, Julia - Křetínský, Jan
PY  - 2023
TI  - Learning Attack Trees by Genetic Algorithms
PB  - Springer
CY  - Lima
SN  - 9783031479625
KW  - genetic algorithms
N2  - Attack trees are a graphical formalism for security assessment. They are particularly valued for their explainability and high accessibility without security or formal methods expertise. They can be used, for instance, to quantify the global insecurity of a system arising from the unreliability of its parts, graphically explain security bottlenecks, or identify additional vulnerabilities through their systematic decomposition. However, in most cases, the main hindrance in the practical deployment is the need for a domain expert to construct the tree manually or using further models. This paper demonstrates how to learn attack trees from logs, i.e., sets of traces, typically stored abundantly in many application domains. To this end, we design a genetic algorithm and apply it to classes of trees with different expressive power. Our experiments on real data show that comparably simple yet highly accurate trees can be learned efficiently, even from small data sets.
ER  -

DORFHUBER, Florian Sebastian, Julia EISENTRAUT a Jan KŘETÍNSKÝ. Learning Attack Trees by Genetic Algorithms. Online. In \textit{Theoretical Aspects of Computing – ICTAC 2023}. Lima: Springer, 2023, s.~55-73. ISBN~978-3-031-47962-5. Dostupné z: https://dx.doi.org/10.1007/978-3-031-47963-2\_{}5.

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