A Cyber Security Impact Analysis Framework for the Electric Smart Grid Grant uri icon


  • The scale and complexity of the smart grid, along with its increased connectivity and automation, make the task of cyber protection particularly challenging. Recently, smart grid researchers and standards bodies have developed technological requirements and potential solutions for protecting cyber infrastructure. However, grid protection remains daunting to asset owners because of resources limitations. Important questions arise when identifying priorities for design and protection: Which cyber components, if compromised, can lead to significant power delivery disruption? What grid topologies are inherently robust to classes of cyber attack? Is the additional information available through advanced cyber infrastructure worth the increased security risk? The goal of this proposal is to study a graph-theoretic dynamical systems approach for modeling the interactions between the cyber and the electricity networks of the smart grid to identify non-cookie-cutter vulnerabilities, the relative physical impact of cyber attacks, and cost-benefit trade offs for potential countermeasures. The electrical network is modeled as a graph where vertices represent distributed generators, intelligent electronic devices, and loads or plug-in hybrids; edges denote physical and operative coupling amongst the respective vertices. Similarly the sensing and communication (cyber) network is depicted as a graph expressing logical and functional interactions for sensing, actuation and cyber attack and mitigation mechanisms. The graphs induce a dynamical systems description of the smart grid conveniently allowing for the coupling of cyber and electrical graphs through dynamical state modeling of the cyber-physical bridge. The degree of modeling abstraction is granular (depending on how the graph vertices are defined) allowing for both extensive and more streamlined assessment subject to available time and computation. The goal of the paradigm is not to produce an exact result of a given cyber attack, but to illustrate possible outcomes and complex interrelationships amongst grid components. We will evaluate the potential of our framework in realistically modeling complex behaviors, in part, through comparison with power system simulations.Intellectual Merit: The overall outcome is a mathematical tool that can be used to design inherently secure smart grid topologies and prioritize security mitigation approaches. The well matched expertise of the team provides a unique opportunity to integrate information security and power system stability concepts to produce a timely and well-suited smart grid security impact analysis approach. The proposed research is the first to seek a formalized analysis methodology with the potential to yield both analytic and empirical results for the assessment of cyber attacks and mitigation strategies on the smart grid. Tighter mathematical modeling of the cyber and electrical processes has the potential to shed greater light on topological design strategies for building a more secure smart grid. Furthermore, the graph-based paradigm facilitates visual representation of results effective in conveying complex interdependent insights to stakeholders.Broader Impacts: This project will enable the early deployment and long-term adoption of secure smart grid technologies, revolutionizing the electricity marketplace while facilitating a smaller ecological footprint. The results of this work will have timely and fundamental implications to power system upgrade and future design that will be disseminated to undergraduate students, high school teachers, researchers, government bodies and industry alike. Education in cyber security of advanced power systems is essentially needed to create security professionals who will work to protect the nation''s critical infrastructures. Recruitment and retention of a diverse student base is facilitated through themed projects, and undergraduate and high school teacher research opportunities.

date/time interval

  • 2010 - 2015