Date of Award

Spring 3-2018

Document Type

Dissertation

Degree Name

Doctor of Science in Information Systems

Department

Computer Science

First Advisor

Dr. Cherie Noteboom

Second Advisor

Dr. David Bishop

Third Advisor

Dr. Ashley Podhradsky

Abstract

Cyber-enabled systems are increasingly ubiquitous and interconnected, showing up in traditional enterprise settings as well as increasingly diverse contexts, including critical infrastructure, avionics, cars, smartphones, home automation, and medical devices. Meanwhile, the impact of cyber attacks against these systems on our missions, business objectives, and personal lives has never been greater. Despite these stakes, the analysis of cyber risk and mitigations to that risk tends to be a subjective, labor-intensive, and costly endeavor, with results that can be as suspect as they are perishable. We identified the following gaps in those risk results: concerns for (1) their repeatability/reproducibility, (2) the time required to obtain them, and (3) the completeness of the analysis per the degree of attack surface coverage.

In this dissertation, we consider whether it is possible to make progress in addressing these gaps with the introduction of a new artifact called “BluGen.” BluGen is an automated platform for cyber risk assessment that employs a set of new risk analytics together with a highly-structured underlying cyber knowledge management repository.

To help evaluate the hypotheses tied to the gaps identified, we conducted a study comparing BluGen to a cyber risk assessment methodology called EVRA. EVRA is representative of current practice and has been applied extensively over the past eight years to both fielded systems and systems under design. We used Design Science principles in the construction and investigation of BluGen, during which we considered each of the three gaps.

The results of our investigation found support for the hypotheses tied to the gaps that BluGen is designed to address. Specifically, BluGen helps address the first gap by virtue of its methods/analytics executing as deterministic, automated processes. In the same way, BluGen helps address the second gap by producing its results at machine speeds in no worse than quadratic time complexity, seconds in this case. This result compares to the 25 hours that the EVRA team required to perform the same analysis. BluGen helps to address the third gap via its use of an underlying knowledge repository of cyber-related threats, mappings of those threats to cyber assets, and mappings of mitigations to the threats. The results show that manual analysis using EVRA covered about 12% of the attack surface considered by BluGen.

Share

COinS