Rui Ning

Research area:

Rui’s research interests mainly fall into the intersection of Cybersecurity and Machine Learning. Machine learning has become an increasingly important tool in various areas during the last decade. As a result, it also has been leveraged in security-critical applications such as face recognition, malware detection, and autonomous vehicle. Therefore, it raises his research interests which consist of two main parts: 1. How to use it as a tool to discover possible vulnerabilities and develop countermeasures. 2. The machine learning model itself, as a sophisticated system, also surfers from adversarial example, backdoor attacks. Rui’s research aims at achieving secure and privacy-preserving AI by exploring its vulnerabilities and building defense mechanisms. Rui’s 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 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 work has 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, Rui foresees a long journey toward secure AI, with many interesting and fundamental research problems to be solved.

Bio:

Rui Ning received BS in Computer Science and Engineering from Lanzhou University, China in 2011, MS 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. He is a current research assistant professor in the Center for Cybersecurity Education & Research, 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.

Education:

Ph.D, 2020, Electrical & Computer Engineering, Old Dominion University

MS, 2016, Computer Science, University of Louisiana at Lafayette

BS, 2011, Computer Science & Engineering, Lanzhou University, China