Cybersecurity
Cybersecurity
Smart manufacturing relies on the industrial Internet of Things (IIoT), but network security remains a major challenge because many industrial protocols lack authentication, confidentiality, and integrity protections. Weak authentication allows attackers to send malicious commands, causing failures or harm. Existing lightweight methods rely on device fingerprints and struggle against evolving attacks.
Subproject 4
A lightweight Mutual Authentication Protocol for Industrial Internet of Things
Goal
To address these shortcomings, the goal of this research is to develop a lightweight mutual authentication protocol between a low-cost resource-constrained device and a resource-rich server that is resistant to various security attacks.
To effectively accomplish this goal:
- The protocol must be lightweight and universal because devices involved are usually resource-constrained and diverse.
- The protocols must defend against the latest but common attacks, i.e., fingerprint reuse attacks and fingerprint mimic attacks, which are not considered in prior authentication schemes.
Outcome
The outcomes from this subproject will pave the way for submitting additional collaborative proposals to funding agencies such as the National Science Foundation, the Department of Energy, the Department of Defense, and the National Institute of Standards and Technology. Furthermore, the expertise developed through this work will position us to pursue industry partnerships and contracts with leading companies in sectors such as manufacturing, energy, transportation, and healthcare, where robust cybersecurity solutions are critical. These collaborations can facilitate technology transfer, support the development of real-world applications, and create pathways for impactful interdisciplinary research that addresses the evolving cybersecurity challenges of modern manufacturing systems.
Principal Investigator
Dr. Mingyan Xiao
Dr. Mingyan Xiao is currently an assistant professor at Computer Science Department, California State Polytechnic University. She graduated with a Ph.D. degree from Computer Science Department, at University of Texas at Arlington. Dr. Xiao is one of the Co-PIs for the CREST-RASM grant.
Research Faculty and Staff