Dr. Shaunak D. Bopardikar, Assistant Professor of Electrical and Computer Engineering, received a two-year, $200,000 grant from the National Science Foundation entitled "Data-driven Attack and Defense Modeling for Cyber-physical Systems".
Modern cyber-physical systems (CPS) such as smart buildings or vaccine storage systems involve several connected devices (phones, sensors, and controllers) that perform multiple objectives including temperature or humidity control. However, attacks such as Stuxnet, or the recent Colonial pipeline attack on industrial CPS have exposed major vulnerabilities, demonstrating the inadequacy of current security techniques to guarantee proper functioning of a CPS. An effective response against smart attackers by pro-actively modeling and responding to attacks on a complex CPS at an ongoing basis, especially with limited data on vulnerabilities in the CPS will be developed in this project.
Using the concept of hybrid attack graphs to capture discrete and domain-specific (physical) dynamics, a data-driven game-theoretic methodology for securing the cyber and physical components of a CPS is the focus of this project. The key idea is to use automated strategies to characterize attacker intent and an integrated defense approach to guide the security strategies of the CPS using a novel combination of game theory and learning. The approach will be evaluated via realistic simulations with emulated data from sensors and actuators of intelligent buildings.
Multiple applications including securing critical infrastructure and supply chains with significant societal impact on public safety will benefit from the project outcomes. Several K-12 outreach and teacher training activities, including hands-on, interactive games between a defender and attacker will be designed to motivate K-12 students to pursue science and engineering. The curriculum of the new graduate course on non-cooperative game theory at MSU will be enriched with new data-driven methods for solving games.
An abstract of the project is available at https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134076&HistoricalAwards=false