MU Engineering faculty are trailblazers in cybersecurity and are creating bold innovations and fostering student learning experiences as well as community outreach activities. The Cybersecurity Center at the University of Missouri has a number of affiliated faculty with active collaborations across diverse units that include: engineering, information technology, business, law, medicine, social science and mathematics.
The University of Missouri possesses an R1 Carnegie Classiﬁcation (Doctoral University: Very high research activity). A link to its 2018 classiﬁcation can be found here.
The College of Engineering is an NSA Center for Academic Excellence. The National Security Agency (NSA) and the Department of Homeland Security (DHS) jointly sponsor the National Centers of Academic Excellence in Cyber Defense (CAE-CD) program. The goal of the program is to reduce vulnerability in our national information infrastructure by promoting higher education and research in cyber defense and producing professionals with cyber defense expertise.
As a member of the Association of American Universities (AAU), the College of Engineering is on the leading edge of innovation, scholarship, and solutions that contribute to scientific progress, economic development, security, and well-being.
Cybersecurity Center faculty and students are working on the cyber-physical system (CPS) /Internet of Things (IoT) security, and robustness analysis of machine learning (ML) algorithms for Industry 4.0 applications. Industry 4.0 is the latest industrial revolution powered by state-of-the-art ML algorithms and IoT sensors. However, sensors and ML algorithms, both are known for their vulnerabilities to cyber-physical attacks. In the context of such complex CPS, these attacks can have catastrophic consequences as they are hard to detect. Our research focuses on analyzing the robustness of such systems at the design phase, and the detection of cyber-physical attacks at runtime.
Secure Multiparty Computation (SMC) offers a way to evaluate a polynomially-bounded functionality based on data from multiple independent parties, without disclosing their own data to the other participating parties. SMC can be used to develop highly secure solutions to protecting personal privacy and data security. Our faculty has been developing privacy-preserving protocols related to data mining and machine learning, friend recommendations in social networks, anonymous communications, and distributed firewalls for enhancing network security. We are also working on novel and efficient designs of SMC primitives, such as comparison and evaluation of arithmetic circuits.