Visible to the public Data-Driven Model-Based Decision-Making - July 2016Conflict 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): William Sanders, Masooda Bashir, David Nicol, and Aad Van Moorsel*

Researchers: Ken Keefe, Mohamad Noureddine, Charles Morriset* and Rob Cain* (*Newcastle Univ., UK)

This refers to Hard Problems, released November 2012.

  • Predictive Security Metrics - System security analysis requires a holistic approach that considers the behavior of non-human subsystem, bad actors or adversaries, and expected human participants such as users and system administrators. We are developing the HITOP modeling formalism to formally describe the behavior of human participants and how their decisions affect overall system performance and security. With this modeling methodology and the tool support we are developing, we will produce quantitative security metrics for cyber-human systems.
  • Human Behavior - Modeling and evaluating human behavior is challenging, but it is an imperative component in security analysis. Stochastic modeling serves as a good approximation of human behavior, but we intend to do more with the HITOP method, which considers a task based process modeling language that evaluates a human's opportunity, willingness, and capability to perform individual tasks in their daily behavior. Partnered with an effective data collection strategy to validate model parameters, we are working to provide a sound model of human behavior.

Papers published in this quarter as a result of this research. Include title, author(s), venue published/presented, and a short description or abstract. Identify which hard problem(s) the publication addressed. Papers that have not yet been published should be reported in region 2 below.

Nothing to report this quarter.


We continue work on a simple prototype of a data collection strategy support tool for the probabilistic model checker PRISM, a tool used at Newcastle. In PRISM we have begun integrating new data collection optimization techniques. First we have implemented functionality for sensitivity analysis, which ranks the input parameters of probabilistic models in terms of their importance on the model output. We are also implementing functionality for uncertainty analysis in terms of how input parameters impact on the uncertainty of the model output. Work is also ongoing to implement functionality for analyzing the variance of model output.

We also continue extending the work submitted in John Mace's PhD thesis looked at providing tools and techniques to analyze the impact of information security policies. We have implemented a tool Workflow Resiliency and Design (WRAD) which allows workflow designers to predict the impact of security policy designs on a workflow's completion. WRAD uses probabilistic models for this and provides a useful case study for our data collection optimization techniques. WRAD also computes optimal change sets for security constraints to assure a given completion rate threshold is reached.