98 citations · 150 across the 14 of their papers we have counts for
14 papers
NetInspector: Measuring and Improving LLM Capabilities for Reliable Intent-Based Networking Policy Generation
Yuxuan Zhang, Hongxin Hu, Guofei Gu
Modern networks are large in scale and heterogeneous in configuration, making manual policy management increasingly impractical. Intent-Based Networking (IBN) addresses this by aut…
Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model
Mingqi Li, Fei Ding, Dan Zhang +3
Pre-trained multilingual language models play an important role in cross-lingual natural language understanding tasks. However, existing methods did not focus on learning the seman…
Understanding and Measuring Robustness of Multimodal Learning
Nishant Vishwamitra, Hongxin Hu, Ziming Zhao +2
The modern digital world is increasingly becoming multimodal. Although multimodal learning has recently revolutionized the state-of-the-art performance in multimodal tasks, relativ…
CARTL: Cooperative Adversarially-Robust Transfer Learning
Dian Chen, Hongxin Hu, Qian Wang +4
Transfer learning eases the burden of training a well-performed model from scratch, especially when training data is scarce and computation power is limited. In deep learning, a ty…
Measuring the Effectiveness of Privacy Policies for Voice Assistant Applications
Song Liao, Christin Wilson, Long Cheng +2
Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly and seamlessly integrating into people's daily lives. The increased reliance on VA services raises priva…
Interpreting Deep Learning-Based Networking Systems
Zili Meng, Minhu Wang, Jiasong Bai +3
While many deep learning (DL)-based networking systems have demonstrated superior performance, the underlying Deep Neural Networks (DNNs) remain blackboxes and stay uninterpretable…
SvTPM: A Secure and Efficient vTPM in the Cloud
Juan Wang, Chengyang Fan, Jie Wang +5
Virtual Trusted Platform Modules (vTPMs) have been widely used in commercial cloud platforms (e.g. Google Cloud, VMware Cloud, and Microsoft Azure) to provide virtual root-of-trust…
Rallying Adversarial Techniques against Deep Learning for Network Security
Joseph Clements, Yuzhe Yang, Ankur Sharma +2
Recent advances in artificial intelligence and the increasing need for powerful defensive measures in the domain of network security, have led to the adoption of deep learning appr…
Vision-based Navigation of Autonomous Vehicle in Roadway Environments with Unexpected Hazards
Mhafuzul Islam, Mahsrur Chowdhury, Hongda Li +1
Vision-based navigation of autonomous vehicles primarily depends on the Deep Neural Network (DNN) based systems in which the controller obtains input from sensors/detectors, such a…
PAM: When Overloaded, Push Your Neighbor Aside!
Zili Meng, Jun Bi, Chen Sun +3
Recently SmartNICs are widely used to accelerate service chains in NFV. However, when the SmartNIC is overloaded, casually migrating vNFs away from SmartNIC to CPU may lead to addi…
CoCo: Compact and Optimized Consolidation of Modularized Service Function Chains in NFV
Zili Meng, Jun Bi, Haiping Wang +2
The modularization of Service Function Chains (SFCs) in Network Function Virtualization (NFV) could introduce significant performance overhead and resource efficiency degradation d…
Cybersecurity Attacks in Vehicle-to-Infrastructure (V2I) Applications and their Prevention
Mhafuzul Islam, Mashrur Chowdhury, Hongda Li +1
A connected vehicle (CV) environment is composed of a diverse data collection, data communication and dissemination, and computing infrastructure systems that are vulnerable to the…
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior
Xiang Zhang, Nishant Vishwamitra, Hongxin Hu +1
We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convo…
CHAOS: an SDN-based Moving Target Defense System
Juan Wang, Feng Xiao, Jianwei Huang +3
The static nature of current cyber systems has made them easy to be attacked and compromised. By constantly changing a system, Moving Target Defense (MTD) has provided a promising…