Policy Evolution with Grammatical Evolution

  • Authors:

  • Yow Tzu Lim;Pau Chen Cheng;John Andrew Clark;Pankaj Rohatgi

  • Affiliations:

  • Department of Computer Science, University of York, UK;Department of Security and Privacy, IBM T J Watson Research Center, USA;Department of Computer Science, University of York, UK;Department of Security and Privacy, IBM T J Watson Research Center, USA

  • Venue:
  • SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
  • Year:
  • 2008

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Abstract

Security policies are becoming more sophisticated. Operational forces will often be faced with making tricky risk decisions and policies must be flexible enough to allow appropriate actions to be facilitated. Access requests are no longer simple subject access object matters. There is often a great deal of context to be taken into account. Most security work is couched in terms of risk management, but the benefits of actions will need to be taken into account too. In some cases it may not be clear what the policy should be. People are often better at dealing with specific examples than producing general rules. In this paper we investigate the use of Grammatical Evolution (GE) to attempt to infer Fuzzy MLS policy from decision examples. This approach couches policy inference as a search for a policy that is most consistent with the supplied examples set. The results show this approach is promising.