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EnglishComputer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graphs analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building and simulations as well as an introductory use case are also outlined.
Conti, V., Ruffo, S., Merlo, A., Migliardi, M., & Vitabile, S. (2018). A bio-inspired approach to attack graphs analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 63-76). Springer Verlag [10.1007/978-3-030-01689-0_5].
| Data di pubblicazione: | 2018 | |
| Titolo: | A bio-inspired approach to attack graphs analysis | |
| Autori: | ||
| Citazione: | Conti, V., Ruffo, S., Merlo, A., Migliardi, M., & Vitabile, S. (2018). A bio-inspired approach to attack graphs analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 63-76). Springer Verlag [10.1007/978-3-030-01689-0_5]. | |
| Abstract: | Computer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graphs analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building and simulations as well as an introductory use case are also outlined. | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/978-3-030-01689-0_5 | |
| URL: | https://www.springer.com/series/558 | |
| Appare nelle tipologie: | 2.01 Capitolo o Saggio |
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| 10.1007:978-3-030-01689-0_5.pdf | Pdf paper | N/A | Administrator Richiedi una copia | |
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