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2017-12-28
Thuraisingham, B., Kantarcioglu, M., Hamlen, K., Khan, L., Finin, T., Joshi, A., Oates, T., Bertino, E..  2016.  A Data Driven Approach for the Science of Cyber Security: Challenges and Directions. 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI). :1–10.

This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe that the three aspects addressed in this paper will form the basis for studying the Science of Cyber Security.

2015-05-01
Chen, K.Y., Heckel-Jones, C.A.C., Maupin, N.G., Rubin, S.M., Bogdanor, J.M., Zhenyu Guo, Haimes, Y.Y..  2014.  Risk analysis of GPS-dependent critical infrastructure system of systems. Systems and Information Engineering Design Symposium (SIEDS), 2014. :316-321.

The Department of Energy seeks to modernize the U.S. electric grid through the SmartGrid initiative, which includes the use of Global Positioning System (GPS)-timing dependent electric phasor measurement units (PMUs) for continual monitoring and automated controls. The U.S. Department of Homeland Security is concerned with the associated risks of increased utilization of GPS timing in the electricity subsector, which could in turn affect a large number of electricity-dependent Critical Infrastructure (CI) sectors. Exploiting the vulnerabilities of GPS systems in the electricity subsector can result to large-scale and costly blackouts. This paper seeks to analyze the risks of increased dependence of GPS into the electric grid through the introduction of PMUs and provides a systems engineering perspective to the GPS-dependent System of Systems (S-o-S) created by the SmartGrid initiative. The team started by defining and modeling the S-o-S followed by usage of a risk analysis methodology to identify and measure risks and evaluate solutions to mitigating the effects of the risks. The team expects that the designs and models resulting from the study will prove useful in terms of determining both current and future risks to GPS-dependent CIs sectors along with the appropriate countermeasures as the United States moves towards a SmartGrid system.