48 citations · 82 across the 5 of their papers we have counts for
6 papers
A Hard-Label Black-Box Evasion Attack against ML-based Malicious Traffic Detection Systems
Zixuan Liu, Yi Zhao, Zhuotao Liu +4
Machine Learning (ML)-based malicious traffic detection is a promising security paradigm. It outperforms rule-based traditional detection by identifying various advanced attacks. H…
Pegasus: A Universal Framework for Scalable Deep Learning Inference on the Dataplane
Yinchao Zhang, Su Yao, Yong Feng +9
The paradigm of Intelligent DataPlane (IDP) embeds deep learning (DL) models on the network dataplane to enable intelligent traffic analysis at line-speed. However, the current use…
Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
Qi Tan, Qi Li, Yi Zhao +3
Federated Learning (FL) trains a black-box and high-dimensional model among different clients by exchanging parameters instead of direct data sharing, which mitigates the privacy l…
Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey
Zhibo Xing, Zijian Zhang, Ziang Zhang +8
Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Decentralize…
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
Jiahui Chen, Yi Zhao, Qi Li +2
Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-base…
AdaptCL: Efficient Collaborative Learning with Dynamic and Adaptive Pruning
Guangmeng Zhou, Ke Xu, Qi Li +2
In multi-party collaborative learning, the parameter server sends a global model to each data holder for local training and then aggregates committed models globally to achieve pri…