NS Seminar

Date and Location

Apr 19, 2017 - 4:45pm to 5:45pm
Bldg 434, Room 122


Continued from last week: A Case Study of a Systematic Attack Design Method for Critical Infrastructure Cyber-Physical Systems (presented by David Grimsman, Electrical and Computer Engineering)

Grimsman, D., Chetty, V., Woodbury, N., Vaziripour, E., Roy, S., Zappala, D., & Warnick, S. (2016, July). A case study of a systematic attack design method for critical infrastructure cyber-physical systems. In American Control Conference (ACC), 2016 (pp. 296-301). IEEE.

As cyber-physical systems continue to become more prevalent in critical infrastructures, security of these systems becomes paramount. Unlike purely cyber systems, cyber-physical systems allow cyber attackers to induce physical consequences. The purpose of this paper is to design a general attack methodology for cyber-physical systems and illustrate it using a case study of the Sevier River System in Central Utah (United States). By understanding such attacks, future work can then focus on designing systems that are robust against them.


Module update: Understanding and Modeling Complex Network Processes (presented by Furkan Kocayusugoglu, Computer Science)

Understanding and modeling complex network processes—such as information cascades in online social networks—is an important task in many real-world applications. With the enormous amount of data generated on the Internet today, it is easy for us to get lost in information overload and fail to see the big picture of how these network processes happen. Can we summarize the spread of information in social networks by a small yet interpretable set of cascading subgraphs, each of which represents a set of connected users frequently participating in the same network processes? In our project, we aim to solve this problem for large-scale social networks and formulate it as a Binary Matrix Factorization with a network constraint. Showing that the problem is NP-hard, we further propose a greedy approximate algorithm as well as two scalable variants of this problem.