(* Equal Contribution and # Corresponding Author)

Working On Papers

  • One paper submitted to ACM International Conference on Multimedia (ACM MM)
  • One paper submitted to IEEE Internet of Things Journal (IOT)
  • One paper submitted to ACM Computing Surveys
  • One paper submitted to IJCAI 2024 (Accepted)
  • One paper submitted to IEEE Computational Intelligence Magzaine (CIM)
  • One paper submitted to IEEE Transactions on Big Data (TBD)
  • One paper submitted to IEEE Transactions on Knowledge and Data Engineering (TKDE)

2024

  • Cao, S., Sun, X., Liu, W., Wu, D., Zhang, J., Li, Y., Luan, T., Gao, L., 2024. EXVUL: Towards Effective and Explainable Vulnerability Detection for IoT Devices. IEEE Internet of Things Journal. (SJR-Q1, CCF-C, IF:10.6) [PDF]
  • Rao, B., Zhang, J., Wu, D., Zhu, C., Sun, X. and Chen, B., 2024. Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive Survey. IEEE Transactions on Artificial Intelligence. [PDF]
  • Wu, D.#, Bai, J., Song, Y., Chen, J., Zhou, W., Xiang, Y. and Sajjanhar, A., 2023, October. FedInverse: Evaluating Privacy Leakage in Federated Learning. In The Twelfth International Conference on Learning Representations. (CORE-A*) [PDF] [CODE]

2023

  • Zhang, J., Liu, Y., Wu, D., Lou, S., Chen, B. and Yu, S., 2023. VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems. Digital Communications and Networks, 9(4), pp.981-989. (SJR-Q1, IF:7.9) [PDF]
  • Wang, N., Chen, J., Wu, D.#, Yang, W., Xiang, Y. and Sajjanhar, A., 2023. Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things. Journal of Information Security and Applications, 75, p.103483 (SJR-Q1, CCF-C, IF:5.6) [PDF]

2022

  • Shen, S., Zhu, T., Wu, D., Wang, W. and Zhou, W., 2022. From distributed machine learning to federated learning: In the view of data privacy and security. Concurrency and Computation: Practice and Experience, 34(16), p.e6002. (SJR-Q3, CCF-C, IF:2.0) [PDF]
  • Chen, J., Guo, Q., Fu, Z., Shang, Q., Ma, H. and Wu, D., 2022, July. Campus Network Intrusion Detection based on Federated Learning. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C) [PDF]
  • Zhao, Y., Chen, J., Zhang, J., Wu, D., Blumenstein, M. and Yu, S., 2022. Detecting and mitigating poisoning attacks in federated learning using generative adversarial networks. Concurrency and Computation: Practice and Experience, 34(7), p.e5906. (SJR-Q3, CCF-C, IF:2.0) [PDF]
  • Wu, D., Wang, N., Zhang, J., Zhang, Y., Xiang, Y. and Gao, L., 2022, July. A Blockchain-based Multi-layer Decentralized Framework for Robust Federated Learning. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C) [PDF]

2021

  • Xie, Y., Chen, B., Zhang, J. and Wu, D., 2021, December. Defending against Membership Inference Attacks in Federated learning via Adversarial Example. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN) (pp. 153-160). IEEE. (CCF-C) [PDF]
  • Chen, J., Wu, D., Zhao, Y., Sharma, N., Blumenstein, M. and Yu, S., 2021. Fooling intrusion detection systems using adversarially autoencoder. Digital Communications and Networks, 7(3), pp.453-460. (SJR-Q1, IF:7.9) [PDF]

2020

  • Zhao, Y., Chen, J., Guo, Q., Teng, J. and Wu, D., 2020, October. Network anomaly detection using federated learning and transfer learning. In Security and Privacy in Digital Economy: First International Conference, SPDE 2020, Quzhou, China, October 30–November 1, 2020, Proceedings (pp. 219-231). Singapore: Springer Singapore. [PDF]
  • Zhang, J., Wu, D., Liu, C. and Chen, B., 2020. Defending poisoning attacks in federated learning via adversarial training method. In Frontiers in Cyber Security: Third International Conference, FCS 2020, Tianjin, China, November 15–17, 2020, Proceedings 3 (pp. 83-94). Springer Singapore. [PDF]
  • Cheng, Z., Zhang, J., Qian, H., Xiang, M. and Wu, D., 2020. A privacy-preserving access control scheme with verifiable and outsourcing capabilities in fog-cloud computing. In Algorithms and Architectures for Parallel Processing: 19th International Conference, ICA3PP 2019, Melbourne, VIC, Australia, December 9–11, 2019, Proceedings, Part I 19 (pp. 345-358). Springer International Publishing. (CORE-B, CCF-C) [PDF]
  • Zhao, Y., Chen, J., Zhang, J., Wu, D., Teng, J. and Yu, S., 2020. PDGAN: A novel poisoning defense method in federated learning using generative adversarial network. In Algorithms and Architectures for Parallel Processing: 19th International Conference, ICA3PP 2019, Melbourne, VIC, Australia, December 9–11, 2019, Proceedings, Part I 19 (pp. 595-609). Springer International Publishing. (CORE-B, CCF-C) [PDF]

2019

  • Zhao, Y., Chen, J., Wu, D., Teng, J. and Yu, S., 2019, December. Multi-task network anomaly detection using federated learning. In Proceedings of the 10th international symposium on information and communication technology (pp. 273-279). [PDF]
  • Zhao, Y., Chen, J., Wu, D., Teng, J., Sharma, N., Sajjanhar, A. and Blumenstein, M., 2019. Network anomaly detection by using a time-decay closed frequent pattern. Information, 10(8), p.262. (SJR-Q2, IF:3.1) [PDF]
  • Zhang, J., Chen, J., Wu, D., Chen, B. and Yu, S., 2019, August. Poisoning attack in federated learning using generative adversarial nets. In 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) (pp. 374-380). IEEE. (CORE-B, CCF-C) [PDF]
  • Wu, D., Chen, J., Sharma, N., Pan, S., Long, G. and Blumenstein, M., 2019, July. Adversarial action data augmentation for similar gesture action recognition. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C) [PDF]
  • Wu, D., Hu, R., Zheng, Y., Jiang, J., Sharma, N. and Blumenstein, M., 2019, July. Feature-dependent graph convolutional autoencoders with adversarial training methods. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. (CORE-B, CCF-C) [PDF]

2018

  • Wu, D., Sharma, N. and Blumenstein, M., 2018, December. Similar Gesture Recognition using Hierarchical Classification Approach in RGB Videos. In 2018 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-7). IEEE. (CORE-Australasian B) [PDF]
  • Wu, D., Sharma, N. and Blumenstein, M., 2018, November. An End-to-End Hierarchical Classification Approach for Similar Gesture Recognition. In 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ) (pp. 1-6). IEEE. (CORE-Australasian B) [PDF]
  • Chou, K.P., Prasad, M., Wu, D., Sharma, N., Li, D.L., Lin, Y.F., Blumenstein, M., Lin, W.C. and Lin, C.T., 2018. Robust feature-based automated multi-view human action recognition system. IEEE Access, 6, pp.15283-15296. (SJR-Q1, IF:3.9) [PDF]

2017

  • Wu, D., Sharma, N. and Blumenstein, M., 2017, May. Recent advances in video-based human action recognition using deep learning: A review. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2865-2872). IEEE. (CORE-B, CCF-C) [PDF]

2015

  • Wen, S., Wu, D., Li, P., Xiang, Y., Zhou, W. and Wei, G., 2015. Detecting stepping stones by abnormal causality probability. Security and Communication Networks, 8(10), pp.1831-1844. (SJR-Q2, IF:1.968) [PDF]

2014

  • Wu, D., Chen, X., Chen, C., Zhang, J., Xiang, Y. and Zhou, W., 2014. On addressing the imbalance problem: a correlated KNN approach for network traffic classification. In Network and System Security: 8th International Conference, NSS 2014, Xi’an, China, October 15-17, 2014, Proceedings 8 (pp. 138-151). Springer International Publishing. (CORE-B) [PDF]

2011

  • Zhang, L., Yu, S., Wu, D. and Watters, P., 2011, November. A survey on latest botnet attack and defense. In 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 53-60). IEEE. (CORE-B, CCF-C) [PDF]

Ph.D. Thesis

  • Wu, D., 2019. Video-based similar gesture action recognition using deep learning and GAN-based approaches (Doctoral dissertation). [PDF]
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