About me

I am an assistant professor in the Department of Computer Science and Engineering at the University of South Florida (USF).

My research interests include federated learning, network intrusion detection, adversarial machine learning, and differential privacy.

Multiple PhD Openings (with assistantship): I am looking for self-motivated students with research interests in any area of cybersecurity or the intersection of AI and security. If this opportunity interests you, reach out to me (ningw@usf.edu), providing your resume and any supplementary material that will help me gain insight into your academic background and research expertise.

Education

• B.S. in Communications Engineering, Beijing University of Posts and Telecommunications, 2015
• M.S. in Electrical and Computer Engineering, Beijing University of Posts and Telecommunications, 2018
• Ph.D in Computer Engineering, Virginia Tech, 2023

News

-Our paper MINDFL: Mitigating the Impact of Imbalanced and Noisy-Labeled Data in Federated Learning With Quality and Fairness-Aware Client Selection,’ is accepted by IEEE Military Communications Conference (MILCOM 2023). (Aug. 2023) pdf

-Our paper Squeezing More Utility via Adaptive Clipping on Deferentially Private Gradients in Federated Meta-Learning is accepted by The Annual Computer Security Applications Conference (ACSAC 2022). (Sep. 2022) pdf code

-Our paper Transferability of Adversarial Examples in Machine Learning-based Malware Detection is accepted by the 2022 IEEE Conference on Communications and Network Security (CNS22). (Aug. 2022) pdf

-Our paper FLARE: Defending Federated Learning against Model Poisoning Attacks via Latent Space Representations is accepted by the 17th ACM ASIA Conference on Computer and Communications Security (AsiaCCS 2022). (Feb. 2022) pdf

-Our paper MANDA: On Adversarial Example Detection for Network Intrusion Detection System is accepted by IEEE Transactions on Dependable and Secure Computing (TDSC). (Feb. 2022) pdf code

-Our paper FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning has been accepted by the 2022 IEEE International Conference on Computer Communications (INFOCOM 2022). (Dec. 2021) pdf code

-Our paper MANDA: On Adversarial Example Detection for Network Intrusion Detection System is accepted by the 2021 IEEE International Conference on Computer Communications (INFOCOM 2021). (Dec. 2020) pdf