Diversifying Network Services Under Cost Constraints for Better Resilience Against Unknown Attacks - Data and Applications Security and Privacy XXX Access content directly
Conference Papers Year : 2016

Diversifying Network Services Under Cost Constraints for Better Resilience Against Unknown Attacks

Daniel Borbor
  • Function : Author
  • PersonId : 1022670
Lingyu Wang
  • Function : Author
  • PersonId : 1004175
Sushil Jajodia
  • Function : Author
  • PersonId : 978046

Abstract

Diversity as a security mechanism has received revived interest recently due to its potential for improving the resilience of software and networks against unknown attacks. Recent work show diversity can be modeled and quantified as a security metric at the network level. However, such an effort does not directly provide a solution for improving the network diversity, and existing network hardening approaches are largely limited to handling previously known vulnerabilities by disabling existing services. In this paper, we take the first step towards an automated approach to diversifying network services under various cost constraints in order to improve the network’s resilience against unknown attacks. Specifically, we provide a model of network services and formulate the diversification requirements as an optimization problem. We devise optimization and heuristic algorithms for efficiently diversifying relatively large networks under different cost constraints. We also evaluate our approach through simulations.
Fichier principal
Vignette du fichier
428203_1_En_21_Chapter.pdf (884.81 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01633677 , version 1 (13-11-2017)

Licence

Attribution

Identifiers

Cite

Daniel Borbor, Lingyu Wang, Sushil Jajodia, Anoop Singhal. Diversifying Network Services Under Cost Constraints for Better Resilience Against Unknown Attacks. 30th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec), Jul 2016, Trento, Italy. pp.295-312, ⟨10.1007/978-3-319-41483-6_21⟩. ⟨hal-01633677⟩
54 View
133 Download

Altmetric

Share

Gmail Facebook X LinkedIn More