Old Dominion University
Rui Ning’s research interests are at the intersection of cybersecurity and machine learning. Machine learning has become an increasingly important tool in various areas during the last decade has been leveraged in security-critical applications such as face recognition, malware detection, and autonomous vehicles. His research interests consist of two main parts: How to use it as a tool to discover possible vulnerabilities and develop countermeasures, and how the machine learning model itself, as a sophisticated system, also suffers from adversarial activity such as backdoor attacks.
Ning’s research aims at achieving secure and privacy-preserving AI by exploring its vulnerabilities and building defense mechanisms. His research contributes to the future development of cybersecurity in two different ways: First, it complements traditional cybersecurity by leveraging machine learning as a tool to discover possible vulnerabilities and developing corresponding countermeasures. He has developed a systematical methodology of adapting the most cutting-edge machine learning techniques to analyze and predict the data collected from mobile and IoT devices.
Second, his research tackles the emerging vulnerabilities and cyberattacks raised by the wide adoption of AI models. Due to the heavily data-driven nature of AI (especially deep learning), a wide-range of AI algorithms is vulnerable to polluted data, adversarial inputs, mimicry attacks, evasion attacks, and poisoning attacks. One of his recent works discovered a new clean label attack, which stealthily and aggressively plants a backdoor in neural networks. Two countermeasures have been investigated to defeat the attack by supervised and unsupervised poison sample detection. Due to AI models' sophistication and fragility, Ning foresees a long journey toward secure AI, with many interesting and fundamental research problems to be solved.
Ning is a research assistant professor in the Center for Cybersecurity Education & Research at Old Dominion University.
He received the Mark Weiser Best Paper Award at the IEEE International Conference on Pervasive Computing and Communication (PerCom) 2018. The IEEE INFOCOM 2019 Best In-session Presentation Award and the ECE Graduate Student Award, Ph.D. Researcher of the Year, 2019.
Ning received his B.S. in computer science and engineering from Lanzhou University, China in 2011, M.S. in computer science from the University of Louisiana at Lafayette in 2016, and Ph.D. in electrical & computer engineering from Old Dominion University in 2020.