Evolving Security Policies

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Yow Tzu Lim",
  title =        "Evolving Security Policies",
  school =       "Computer Science, University of York",
  year =         "2010",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  URL =          "http://etheses.whiterose.ac.uk/id/eprint/1612",
  URL =          "http://etheses.whiterose.ac.uk/1612/3/thesis.pdf",
  size =         "212 pages",
  abstract =     "As computer system size and complexity grow,
                 formulating effective policies require more
                 sophistication. There are many risk factors that need
                 to be considered, some of which may be in conflict.
                 Inevitably, unpredictable circumstances that demand
                 decisions will arise during operation. In some cases an
                 automated response may be imperative; in other cases
                 these may be ill-advised. Manual decisions are often
                 made that override the current policy and serve
                 effectively to redefine it. This matter is further
                 complicated in highly dynamic operational environments
                 like mobile ad-hoc networks, in which the risk factors
                 may be changing continually. Thus, security policies
                 must be able to change and adapt to the operational
                 needs. This study investigates the potential of
                 evolutionary algorithms as a tool in determining the
                 optimal security policies that suit such environments.
                 This thesis reviews some fundamental concepts in
                 related domains. It presents three applications of
                 evolutionary algorithms in solving problems that are of
                 direct relevance. These include the inference of
                 security policies from decision examples, the dynamic
                 adaptation of security policies, and the optimisation
                 of security policies for a specific set of missions.
                 The results show that the inference approaches based on
                 evolutionary algorithms are very promising.

                 The thesis concludes with an evaluation of the work
                 done, the extent to which the work justifies the thesis
                 hypothesis and some possible directions on how
                 evolutionary algorithms can be applied to address a
                 wider range of relevant problems in the domain of
  notes =        "Supervisor John A. Clark",

Genetic Programming entries for Yow Tzu Lim