Learning Autonomic Security Reconfiguration Policies

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Juan E. Tapiador and John A. Clark",
  title =        "Learning Autonomic Security Reconfiguration Policies",
  booktitle =    "IEEE 10th International Conference on Computer and
                 Information Technology (CIT)",
  year =         "2010",
  pages =        "902--909",
  month =        jun,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CIT.2010.168",
  abstract =     "We explore the idea of applying machine learning
                 techniques to automatically infer risk-adaptive
                 policies to reconfigure a network security architecture
                 when the context in which it operates changes. To
                 illustrate our approach, we consider the case of a
                 MANET where nodes carrying sensitive services (e.g.,
                 web servers, key repositories, etc.) should consider
                 relocating themselves into a different node to
                 guarantee proper functioning. We use simulation to
                 derive properties from a candidate policy, and then
                 apply Genetic Programming and Multi-Objective
                 Optimisation techniques to search for optimal
                 candidates. The inferred policies take the form of
                 risk-aware service relocation algorithms that
                 autonomously dictate when and how to relocate services
                 with the aim of keeping risk to a minimum. Since
                 security policies often have implications in dimensions
                 other than security, we force the learning process to
                 consider also the consequences (performance, usability)
                 of a given policy.",
  notes =        "Also known as \cite{5578469}",

Genetic Programming entries for Juan Manuel Estevez-Tapiador John A Clark