CV

Ning’s CV

Education

• B.S. in Communications Engineering, Beijing University of Posts and Telecommunications, 2011-2015
• M.S. in Electrical and Computer Engineering, Beijing University of Posts and Telecommunications, 2015-2018
• Ph.D in Computer Engineering, Virginia Tech, 2018-2023

Work experience

• Assistant Professor (08/2023-present)
  • Department of Computer Science and Engineering, University of South Florida
• Graduate Research Assistant (08/2018-05/2023)
  • CNSR@VT
• 2022 Summer Intern 2022: Security SDE
  • Bytedance
  • Duties included: Building Intelligence for Access Control

Skills

Publications

PriRoster: Privacy-preserving Radio Context Attestation in Cognitive Radio Networks

R. Zhang, N. Zhang, N. zhang, Z. Yan, W. Lou and Y.T. Hou, “PriRoster: Privacy-preserving Radio Context Attestation in Cognitive Radio Networks,” DySPAN, Nov 11-14, 2019, Newark, USA.

MANDA: On Adversarial Example Detection for Network Intrusion Detection System

N. Wang, Y. Chen, Y. Hu, W. Lou and Y.T. Hou, “MANDA: On Adversarial Example Detection for Network Intrusion Detection System,” IEEE INFOCOM 2021, May 10-13, 2021, online conference.

MANDA: On Adversarial Example Detection for Network Intrusion Detection System

N. Wang, Y. Chen, Y. Xiao, Y. Hu, W. Lou and Y.T. Hou, “MANDA: On Adversarial Example Detection for Network Intrusion Detection System,” in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2022.3148990.

FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning

N. Wang, Y. Chen, Y. Hu, W. Lou and Y.T. Hou, “FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning,” IEEE INFOCOM 2022, May 2-5, 2022, virtual conference.

FLARE: Defending Federated Learning against Model Poisoning Attacks via Latent Space Representations

N. Wang, Y. Xiao, Y. Chen, Y. Hu, W. Lou and Y.T. Hou, “FLARE: Defending Federated Learning against Model Poisoning Attacks via Latent Space Representations,” Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, May 30–June 3, 2022, Nagasaki, Japan. ACM, 13 pages. https://doi.org/10.1145/3488932.3517395.

Transferability of Adversarial Examples in Machine Learning-based Malware Detection

Y. Hu, N. Wang, Y. Chen, W. Lou and Y.T. Hou, “Transferability of Adversarial Examples in Machine Learning-based Malware Detection,” CNS, Oct 3-5, 2022, Austin, USA.

Squeezing More Utility via Adaptive Clipping on Deferentially Private Gradients in Federated Meta-Learning

N. Wang, Y. Xiao, Y. Chen, N. Zhang, W. Lou and Y.T. Hou, “Squeezing More Utility via Adaptive Clipping on Deferentially Private Gradients in Federated Meta-Learning,” ACSAC, Dec 5-9, 2022, Austin, USA.

Talks

FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning

May 05, 2022

Conference proceedings talk at IEEE INFOCOM 2022, Recorded Talk for Virtual Conference

Flare: Defensing Federated Learning against Model Poisoning Attacks via Latent Space Representations

June 02, 2022

Talk at AsiaCCS 2022, Recorded, the talk is shared at the conference in Nagasaki, Japan

Teaching