A novel approach to determining real-time risk probabilities in critical infrastructure industrial control systems

Critical Infrastructure Industrial Control Systems are substantially different from their more common and ubiquitous information technology system counterparts. Industrial control systems, such as distributed control systems and supervisory control and data acquisition systems that are used for controlling the power grid, were not originally designed with security in mind. Geographically dispersed distribution, an unfortunate reliance on legacy systems and stringent availability requirements raise significant cybersecurity concerns regarding electric reliability while constricting the feasibility of many security controls. Recent North American Electric Reliability Corporation Critical Infrastructure Protection standards heavily emphasize cybersecurity concerns and specifically require entities to categorize and identify their Bulk Electric System cyber systems; and, have periodic vulnerability assessments performed on those systems. These concerns have produced an increase in the need for more Critical Infrastructure Industrial Control Systems specific cybersecurity research. Industry stakeholders have embraced the development of a large-scale test environment through the Department of Energy’s National Supervisory Control and Data Acquisition Test-bed program; however, few individuals have access to this program. This research developed a physical industrial control system test-bed on a smaller-scale that provided an environment for modeling a simulated critical infrastructure sector performing a set of automated processes for the purpose of exploring solutions and studying concepts related to compromising control systems by way of process-tampering through code exploitation, as well as, the ability to passively and subsequently identify any risks resulting from such an event. Relative to the specific step being performed within a production cycle, at a moment in time when sensory data samples were captured and analyzed, it was possible to determine the probability of a real-time risk to a mock Critical Infrastructure Industrial Control System by comparing the sample values to those derived from a previously established baseline. This research achieved such a goal by implementing a passive, spatial and task-based segregated sensor network, running in parallel to the active control system process for monitoring and detecting risk, and effectively identified a real-time risk probability within a Critical Infrastructure Industrial Control System Test-bed. The practicality of this research ranges from determining on-demand real-time risk probabilities during an automated process, to employing baseline monitoring techniques for discovering systems, or components thereof, exploited along the supply chain.