The College’s cybersecurity research is focused on five major objectives: design of new systems that are inherently secure; protection of extremely complex systems, such as the web, mobile systems, and critical infrastructure; application of mathematical logic for systems assurance; analysis and detection of malware and other cyber attacks; and data mining and anomaly-detection algorithms to detect data sequences that indicate undesirable cyber behavior.
A truly unique characteristic of the College’s cybersecurity research group is that the collection of research strengths allows our faculty to address security problems throughout the vertical structure of cyber systems from the system components to the user experience. This structure includes hardware, system software, application software, data security, and command and control. It is only through a systems approach to cybersecurity that the emergent effects that arise from combining components into complex systems can be understood to avoid catastrophic failures.
With this emphasis on overall system security and assurance, our faculty are particularly active in application areas in which there is a need for high confidence that a system behaves correctly, including its availability, integrity, confidentiality, and scalability. Examples of systems of this kind include defense and national security operations, medical record systems, financial and banking systems, and critical infrastructure. Indeed, the faculty has a long record of working with the defense and banking industries.
Our faculty are passionate about integrating this research into education. Based on our research and partnerships with government and industry, we have successfully piloted and continue to develop innovative educational programs at the undergraduate and graduate levels that equip students to engineer secure systems with assurance in mind. Our faculty have developed security education materials that are currently being used by over 200 universities in 26 countries.
Kevin Du conducts research on web security, privacy protection, physical layer security, and also the application of data mining methodologies to cyber-security.
Chilukuri Mohan applies network-theoretic analyses, machine learning, pattern recognition, and data mining algorithms for anomaly detection and the identification of potential fraud from data sets describing the behavior of people, e.g., financial data.