Scientific Understanding of Policy Complexity - October 2015

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, Matt Witte

HARD PROBLEM(S) ADDRESSED

• 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. 

PUBLICATIONS
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.

ACCOMPLISHMENT HIGHLIGHTS

• We have conducted another round of human subject study on understanding firewall policies with a longer policy and a list of more sophisticated questions. Motivated by results from this study, we have obtained devices to measure electroencephalogram (EEG) signals and are designing experiments to use these devices to compare the mental workload users experience when using our policy langauge and when using the original language. 

• We have completed the inspection of real policy misconfigurations from GitHub, and have found several interesting ways policies are misconfigured. We intend to build a tool or environment that helps developers detect and avoid policy misconfigurations.