Wenjing Lou

About

Wenjing Lou is a professor in the Department of Computer Science at Virginia Tech. She holds a Ph.D. in electrical and computer engineering from the University of Florida. Her research interests include cybersecurity, wireless networks, cyber-physical systems security, adversarial machine learning, and applied cryptography. She is associated with the Virginia Tech Research Center in Arlington, VA, and has multiple contact points including her email wjlou@vt.edu and phone number (703) 538-3774. Her professional activities are centered around advancing knowledge and solutions in the fields of cybersecurity and network security.

Research topics

• Artificial Intelligence
• Computer Science
• Computer Security
• Machine Learning
• Mathematical optimization
• Distributed computing
• Computer network
• Telecommunications

Selected publications

• NPMML: A Framework for Non-interactive Privacy-preserving Multi-party Machine Learning

  IEEE Transactions on Dependable and Secure Computing · 2020 · 92 citations

  In the recent decade, deep learning techniques have been widely adopted for founding artificial Intelligent applications, which led to successes in many data analysis tasks, such as risk assessment, medical predictions, and face recognition. Since the effectiveness of deep learning is directly proportional to the amount of data available, a large-scale collection of massive data is essential. Considering privacy and security concerns often prevent data owners from contributing sensitive data for…

  DOI

• A Deep-Reinforcement-Learning-Based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR

  IEEE Internet of Things Journal · 2020 · 88 citations

  Senior authorCorresponding

  This article investigates the dynamic multiplexing of enhanced mobile broadband (eMBB) and ultrareliable and low latency communications (URLLC) on the same channel in 5G NR. Due to significant difference in transmission time scale, URLLC employs a preemptive puncturing technique to multiplex its traffic onto eMBB traffic for transmission. The optimization problem to solve is to minimize the adverse impact of such preemptive puncturing on eMBB users. We present DEMUX - a model-free deep reinforce…

  DOI

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

  IEEE Transactions on Dependable and Secure Computing · 2025-02-20 · 14 citations

  article

  Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of adequate security in IoT devices renders IoT systems vulnerable to various network-based cyberattacks, potentially causing severe damage. Recent works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devi…

  PublisherDOI

• Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction

  2025-01-01 · 5 citations

  articleOpen accessSenior author
  PublisherDOI

• <scp>VehiGAN</scp> : Generative Adversarial Networks for Adversarially Robust V2X Misbehavior Detection Systems

  ACM Transactions on Cyber-Physical Systems · 2025-06-24 · 4 citations

  articleSenior author

  Vehicle-to-Everything (V2X) communication enables vehicles to communicate with other vehicles and roadside infrastructure, enhancing traffic management and improving road safety. However, the open and decentralized nature of V2X networks exposes them to various security threats, especially misbehaviors, necessitating a robust Misbehavior Detection System (MBDS). While Machine Learning (ML) has proved effective in different anomaly detection applications, the existing ML-based MBDSs have shown li…

  PublisherDOI

Recent grants

• EAGER: A Novel Approach to Achieve Real-time Wireless Network Optimization

  NSF · $300k · 2018–2020

• CT-ISG: Broadcast/Multicast Security in Multi-User Wireless Sensor Networks

  NSF · $337k · 2007–2011

• CPS: Medium: S2Guard: Building Security and Safety in Autonomous Vehicles via Multi-Layer Protection

  NSF · $1.1M · 2019–2024

Frequent coauthors

• Y. Thomas Hou

  Virginia Tech

  191 shared

• Jie Yang

  86 shared

• Donald R. Brown

  Worcester Polytechnic Institute

  84 shared

• Kai Zeng

  Kunming University of Science and Technology

  76 shared

• Kui Ren

  71 shared

• Jin Li

  Guangzhou University

  59 shared

• Yi Shi

  State Key Laboratory of Quantum Optics and Quantum Optics Devices

  47 shared

• Kai Zeng

  36 shared

Education

• Ph.D., Department of Electrical and Computer Engineering

  University of Florida

  2003

Similar researchers at Virginia Tech

• Doug Bowmanh-56
• Wu-chun Fengh-46
• Siwei Caoh-43