MU 糖心Vlog传媒 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.
Research Rating
The University of Missouri possesses an聽R1 Carnegie Classi铿乧ation (Doctoral University: Very high research activity).
The College of 糖心Vlog传媒 is an聽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聽Association of American Universities (AAU), the College of 糖心Vlog传媒 is on the leading edge of innovation, scholarship, and solutions that contribute to scientific progress, economic development, security, and well-being.
Focusing on research and education in the US national interest is a priority for the Cybersecurity Center faculty and students.聽Several research聽grants and contracts聽have been secured for many cybersecurity projects with聽US DOD and Intelligence agencies聽including the聽US Naval Research Laboratory, US Army Research Laboratory and National Security Agency, as well as the National Science Foundation and US Department of Energy. Cybersecurity Center faculty and students have a strong publication record in competitive, top conferences and journals聽in interdisciplinary cyber security (e.g.,聽Journal of Computer Security,聽ACM Computer and Communications Security, IEEE INFOCOM, IEEE Transactions on Services Computing, IEEE Transactions in Network Service Management, IEEE Transactions on Cloud Computing,聽among others).
Research Topics
Cybersecurity Center faculty and students are working on聽usable cybersecurity聽in order to systematically study the synergies as well as constraints in balancing resilience (security) and user experience (performance/usability). At聽MU,聽projects funded by the National Science Foundation are underway to聽investigate聽new foundations and architectures for usable cybersecurity by considering the CIA triad (Confidentiality, Integrity and Availability) requirements. Our goal is to advance the state-of- the-art in usable cybersecurity relating to access control, anomaly detection, defense using pretense, and other cyber defense concepts. We particularly focus on the context of cloud-hosted application architectures and services delivery within operational environments of media content providers (e.g., just-in-time news feeds, video streaming,聽health information sharing,聽video gaming, social virtual reality).
Cybersecurity Center faculty and students are working on聽model-checking and formal methods in the automated verification of safety and security of programs/protocols/database solutions. The automated analysis of such computer programs/protocols/database solutions聽pose difficult聽challenges that are part of on-going investigations.聽Our focus is on techniques that employ randomization and our goal is to develop聽novel practical formal analysis methods that reflect the partial observability on the part of the attacker聽in order聽to faithfully analyze such systems聽safety and security.聽These projects are funded by National Science Foundation.
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.
Cybersecurity Center faculty聽and students are working on privacy issues in social networks.聽In聽ongoing聽projects funded by National Science Foundation, investigations are underway to聽help聽preserve privacy during online image sharing.聽Our聽objective is to design a comprehensive framework namely iPrivacy (image Privacy), which leverages multiple factors to automatically recommend personalized privacy settings for photo sharing, and hence releases users burden and provides better privacy practice. Unlike existing works on privacy recommendation that focus only on the privacy aspect, the iPrivacy system聽is聽the first one that seamlessly integrates techniques from two different domains: the image processing and privacy management, to provide a complete policy recommendation system that is automatic, easy to use and efficient in the real social networking environment whereby huge amounts of photos are shared.
Cybersecurity Center faculty and students聽are working on a sound logical representation of complexity-theoretic, probabilistic formulation of security (provable security).聽Such a representation聽is amenable to automated verification, and can also be used to find attacks when proving security fails.聽Efforts are underway to build聽a library of first-order axioms that express various standard hardness assumptions such as the discrete-logarithm, or Diffie-Hellman assumptions, as well as axioms of standard complexity-theoretic properties of cryptographic primitives, such as security against chosen plaintext or ciphertext attacks.聽We employ such聽axioms to verify various properties of protocols, such as secrecy, anonymity, authentication, etc, even of complex ones such as voting protocols.聽Effectiveness of our approach can be seen from our discovery of聽new attacks聽in聽even simple protocols such as the Needham-Schroeder-Lowe protocol.
Secure Multiparty Computation (SMC) offers聽 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, 聽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.