Visible to the public Scientific Understanding of Policy Complexity - April 2015Conflict Detection Enabled

Public Audience
Purpose: To highlight project progress. Information is generally at a higher level which is accessible to the interested public. All information contained in the report (regions 1-3) is a Government Deliverable/CDRL.

PI(s):  Ninghui Li, Robert Proctor, Emerson Murphy-Hill
Researchers: Jing Chen, Haining Chen, Manish Singh



  • Policy-Governed Secure Collaboration -  Security policies can be very complex, in the sense that they are difficult for humans to understand and update.  We are interested in two kinds of complexity measures.  The first is a measure of the inherent complexity of a policy.  The second is a measure of the representational complexity, which is the complexity of a particular way to encode the policy.  It is desirable to have a scientific understanding of both kinds of complexity. 
  • Human Behavior - Our policy complexity is based on how easy for humans to understand and write policies.  There is thus a human behavior aspect to it. 

Report papers written as a results of this research. If accepted by or submitted to a journal, which journal. If presented at a conference, which conference.



  • We have developed the Tri-Modular Firewall Language (TMFL) for specifying firewall policies.  This enables one to specify firewall policies in a modularized fashion, and suggest a metric measuring the depth of the hierarchy for the complexity of a policy.
  • The IRB for a human subject study on the understandability of firewall policies presented in different ways has been approved.  The study has been finalized. 
  • Began a study that investigates the mistakes software developers make when making policy specifications using Spring Security Annotations. A pilot of the study revealed about a dozen types of policy specification mistakes.