| Poisoning attack in federated learning using generative adversarial nets J Zhang, J Chen, D Wu, B Chen, S Yu TrustCom 2019, 2019 | 207 | 2019 |
| Recent advances in video-based human action recognition using deep learning: A review D Wu, N Sharma, M Blumenstein IJCNN 2017, 2017 | 176 | 2017 |
| Multi-task network anomaly detection using federated learning Y Zhao, J Chen, D Wu, J Teng, S Yu SOICT 2019, 2019 | 169 | 2019 |
| A survey on latest botnet attack and defense L Zhang, S Yu, D Wu, P Watters TrustCom 2011, 2011 | 119 | 2011 |
| From distributed machine learning to federated learning: In the view of data privacy and security S Shen, T Zhu, D Wu, W Wang, W Zhou Concurrency and Computation: Practice and Experience 34 (16), e6002, 2022 | 89 | 2022 |
| PDGAN: A novel poisoning defense method in federated learning using generative adversarial network Y Zhao, J Chen, J Zhang, D Wu, J Teng, S Yu ICA3PP 2019, 2020 | 89 | 2020 |
| Robust feature-based automated multi-view human action recognition system KP Chou, M Prasad, D Wu, N Sharma, DL Li, YF Lin, M Blumenstein, ... IEEE Access 6, 15283-15296, 2018 | 73 | 2018 |
| Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks Y Zhao, J Chen, J Zhang, D Wu, M Blumenstein, S Yu Concurrency and Computation: Practice and Experience 34 (7), e5906, 2022 | 50 | 2022 |
| Fooling intrusion detection systems using adversarially autoencoder J Chen, D Wu, Y Zhao, N Sharma, M Blumenstein, S Yu Digital Communications and Networks 7 (3), 453-460, 2021 | 37 | 2021 |
| VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems J Zhang, Y Liu, D Wu, S Lou, B Chen, S Yu Digital Communications and Networks 9 (4), 981-989, 2023 | 25 | 2023 |
| Defending poisoning attacks in federated learning via adversarial training method J Zhang, D Wu, C Liu, B Chen FCS 2020, 2020 | 24 | 2020 |
| Network anomaly detection using federated learning and transfer learning Y Zhao, J Chen, Q Guo, J Teng, D Wu SPDE 2020, 2020 | 21 | 2020 |
| Adversarial action data augmentation for similar gesture action recognition D Wu, J Chen, N Sharma, S Pan, G Long, M Blumenstein IJCNN 2019, 2019 | 21 | 2019 |
| On addressing the imbalance problem: a correlated KNN approach for network traffic classification D Wu, X Chen, C Chen, J Zhang, Y Xiang, W Zhou NSS 2014, 2014 | 19 | 2014 |
| A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning D Wu, N Wang, J Zhang, Y Zhang, Y Xiang, L Gao IJCNN 2022, 2022 | 9 | 2022 |
| Defending against membership inference attacks in federated learning via adversarial example Y Xie, B Chen, J Zhang, D Wu MSN 2021, 2021 | 8 | 2021 |
| Privacy inference attack and defense in centralized and federated learning: A comprehensive survey B Rao, J Zhang, D Wu, C Zhu, X Sun, B Chen IEEE Transactions on Artificial Intelligence, 2024 | 4 | 2024 |
| Campus network intrusion detection based on federated learning J Chen, Q Guo, Z Fu, Q Shang, H Ma, D Wu IJCNN 2022, 2022 | 4 | 2022 |
| Network anomaly detection by using a time-decay closed frequent pattern Y Zhao, J Chen, D Wu, J Teng, N Sharma, A Sajjanhar, M Blumenstein Information 10 (8), 262, 2019 | 4 | 2019 |
| Feature-dependent graph convolutional autoencoders with adversarial training methods D Wu, R Hu, Y Zheng, J Jiang, N Sharma, M Blumenstein IJCNN 2019, 2019 | 4 | 2019 |
