David M. Nicol
Endowed Chair in Engineering at the University of Illinois Urbana-Champaign
- February 15, 2023
- 2:00 PM
- CASE 349 & Zoom
David M. Nicol is the Herman M. Dieckamp Endowed Chair in Engineering at the University of Illinois Urbana-Champaign, a member of the Department of Electrical and Computer Engineering, and Director of UIUC’s Information Trust Institute. Nicol holds a B.A. in Mathematics from Carleton College, and M.S. and Ph.D. degrees in Computer Science from the University of Virginia. Prior to joining the University of Illinois he held faculty positions in the Departments of Computer Science at the College of William and Mary, and Dartmouth College. He was elected Fellow of the IEEE and Fellow of the ACM for his research contributions and is the inaugural recipient of the ACM SIGSIM Distinguished Contributions Award. His research interest encompass modeling and analysis of computer systems used to control critical infrastructures, and he is a co-founder of the company Network Perception, whose products are widely used in the elec
A key issue in assessing the security state of a complex computer network is determining its connectivity, in some detail. For example, NERC-CIP audit requirements in the electric power grid requires utilities to identify and document all connections that are possible to devices whose operations are essential to proper delivery of power system services. Failure to comply with NERC-CIP requirements can (and has) led to very large fines. In this talk we describe the challenges and a solution wherein the configuration files of firewalls, switches, and routers are used to build a model where all connectivity permitted by the configurations can be computed. We go further to address a problem of performing that analysis in the cloud, as a service, on an anonymized transformation of the model which ensures that sensitive IP map information does not touch the cloud (even in encrypted form), but still the same connectivity results are discovered and reported as with the un-anonymized model.