Quantitative Security Metrics for Cyber-Human Systems
funded through the U.S. National Security Agency SoS Lablet
researchers: William H. Sanders, David M. Nicol, Jim Blythe (University of Southern California), and Sean W. Smith (Dartmouth College)
Making sound security decisions when designing, operating, and maintaining a complex enterprise-scope system is a challenging task. Quantitative security metrics have the potential to provide valuable insight on system security and to aid in security decisions. To produce model-based quantitative security metrics, we developed the ADversary VIew Security Evaluation (ADVISE) method and implemented it in the prototype tool Möbius-SE (Möbius Security Edition), which is suitable for use by security modeling experts. Our goal in this project is to extend the ADVISE method and tool to explicitly account for the behavior of human users as part of the system. While cyber security models traditionally model the behavior of the attacker, they usually do not explicitly account for the behavior of the users of a system, and how that use can create or eliminate system vulnerabilities.
Increasingly, accumulated cyber security data indicate that system users can play an important role in the creation or elimination of cyber security vulnerabilities. Thus, there is a need for cyber security analysis tools that take into account the actions and decisions of human users.
We are 1) developing a Möbius-SE-compatible, process-oriented modeling formalism for modeling how human users interact with systems, using the concept of a human decision point to explicitly represent decisions that affect the security of a system; 2) implementing the formalism as an atomic model editor in Möbius-SE that generates models that can interact with other Möbius-SE models, e.g., models of the systems itself and the attacker; and 3) demonstrating the use of the implemented tool in a variety of government- and industry-motivated case studies, as suggested by our sponsor and industry partners (HP and GE).