(* Equal Contribution, # Corresponding Author. Rank and IF refer to the acceptance time)

Selected Publications

Trustworthy Artifical Intelligence/Machine Learning

  • Chen, T., Xiang, W., Han, K., Lu, Y., Wu, D., Liu, G. and Kompella, R.R., 2026. GIFSplat: Generative Prior-Guided Iterative Feed-Forward 3D Gaussian Splatting from Sparse Views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 26031-26040). (CORE-A*, CCF-A)[Accept Rate: 25.42% (4090/16092)] [PDF]
  • Do, M.K., Xiang, W., Han, K., Wu, D., Phan, K., Chen, Y.P.P., Liu, G. and Kompella, R.R., 2026. Spectrally Distilled Representations Aligned with Instruction-Augmented LLMs for Satellite Imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6453-6463). (CORE-A*, CCF-A)[Accept Rate: 25.42% (4090/16092)] [PDF] [CODE]
  • Hou, Y., Lv, D., Liwang, M., Wang, J., Li, Z., Xia, X., Wu, D., Tian, Y., Quek, T.Q. and Wang, X., 2026. AsyncFed-SAR: Towards Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition. IEEE Transactions on Mobile Computing. (SJR-Q1, CCF-A, IF:8.1) [PDF]
  • Li, K., Li, W., Wu, D.*, Yang, L., Bai, J., Jia, J. and Xue, J., 2026, March. Cross-modal unlearning via influential neuron path editing in multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (Vol. 40, No. 42, pp. 35589-35597). (CORE-A*, CCF-A)[Accept Rate: 17.6% (4167/23680)] [PDF] [CODE]
  • Bai, J.*, Song, Y.*, Wu, D.*, Sajjanhar, A., Xiang, Y., Zhou, W., Tao, X., Li, Y. and Li, Y., 2025, August. A unified solution to diverse heterogeneities in one-shot federated learning. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (KDD) (pp. 71-82). (CORE-A*, CCF-A)[Accept Rate: 14% (1632/11442)] [PDF] [CODE]
  • Sun, X., Zhou, M., Cao, S., Wu, X., Bo, L., Wu, D., Li, B. and Xiang, Y., 2025. HgtJIT: Just-in-Time Vulnerability Detection Based on Heterogeneous Graph Transformer. IEEE Transactions on Dependable and Secure Computing. (SJR-Q1, CCF-A, IF:7.5) [PDF] [CODE]
  • Jia, J., Yu, J., Wu, D.#, Wu, C., Zhu, H. and Wang, L., 2025, August. Prompt as a Double-Edged Sword: A Dynamic Equilibrium Gradient-Assigned Attack against Graph Prompt Learning. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (KDD) (pp. 1049-1060). (CORE-A*, CCF-A)[Accept Rate: 14% (1632/11442)] [PDF]
  • Cai, B., Xiang, Y., Gao, L., Wu, D., Zhang, H., Jin, J. and Luan, T., 2024. From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding. IEEE Transactions on Knowledge and Data Engineering. (SJR-Q1, CCF-A, IF:8.9) [PDF]
  • Zhang, J.*, Zhu, C.*, Wu, D.*, Sun, X, Yong, J. and Long, G., 2024. BADFSS: Backdoor Attacks on Federated Self-Supervised Learning. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI). (CORE-A*, CCF-A)[Accept Rate: 14.0% (791/5651)] [PDF]
  • Wu, D., Bai, J., Song, Y., Chen, J., Zhou, W., Xiang, Y. and Sajjanhar, A., 2024, October. FedInverse: Evaluating Privacy Leakage in Federated Learning. In The Twelfth International Conference on Learning Representations (ICLR). (CORE-A*)[Accept Rate: 30.52% (2260/7404)] [PDF] [CODE]

Security and Privacy

  • Li, Y., Chen, W., Zhu, C., Zhang, J., Ge, C., Wu, D. and Long, G., 2026. Meta-FC: Meta-Learning with Feature Consistency for Robust and Generalizable Watermarking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 17420-17429). (CORE-A*, CCF-A)[Accept Rate: 25.42% (4090/16092)] [PDF]
  • Jia, J., Song, J., Yu, J., Guo, J., Jia, X., Wu, D., Yuan, Y. and Cheng, G., 2026, March. PAGPL: Privacy-Aware Graph Prompt Learning Scheme via Adaptive Perturbation-Estimated Topology Recovery. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (Vol. 40, No. 27, pp. 22247-22255). (CORE-A*, CCF-A)[Accept Rate: 17.6% (4167/23680)] [PDF] [CODE]
  • Huang, L., Mao, X., Zhou, L., Wu, D.#, Gao, L. and Luan, T.H., 2025. FiDD: Secure Fine-Grained Deduplication and Dynamic Auditing Scheme for Cloud Storage. IEEE Transactions on Networking. (SJR-Q1, CCF-A, IF:3.6) [PDF]
  • Gao, X., Sun, X., Cao, S., Huang, K., Wu, D., Liu, X., Lin, X., Xiang, Y., 2025. MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem. In Proceedings of the Thirty-Forth Usenix Security Symposium (Usenix Security). (CORE-A*, CCF-A)[Accept Rate: 17.1% (407/2385)] [PDF]
  • Zhang, J.*, Li, H.*, Wu, D.*, Sun, X., Lu, Q. and Long, G., 2025, April. Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models. In Proceedings of the ACM on Web Conference (WWW) (pp. 147-157). (CORE-A*, CCF-A)[Accept Rate: 19.8% (409/2062)] [PDF] [CODE]

AI4Science

  • Jafari, P., Ye, G., Wang, J., Chu, T., Hou, B., Wu, D., Huo, S., Wang, H., Sun, Z. and Song, P., 2026. Designing flame retardants for epoxy resins with generative AI for preventing battery thermal runaway propagation. Journal of Materials Science & Technology. (SJR-Q1, IF:14.3) [PDF]
  • Ma, X., Yin, Z., Cao, Z., Shi, B., Yan, X., Wu, D., Wang, Y., Dai, C. and Hong, M., 2025. Machine learning assisted photocurrent wavelength classification in a self-powered broadband photoelectrochemical photodetector based on Bi2Te0.6Se2.4/MoSe2 heterojunctions. Chemical Engineering Journal, p.169645. (SJR-Q1, IF:13.2) [PDF]
  • Li, Y., Liu, R., Ji, Z., Gao, L., Wang, X., Zhang, J., Hu, L., Qu, Y., Bai, J., Wu, D.# and Han, S., 2025. Predicting lymphatic transport potential using graph transformer based on limited historical data from in vivo studies. Journal of Controlled Release, p.113847. (SJR-Q1, IF:10.5) [PDF]

Full Publication List

  • [J47] Liao, X., Si, Z., Liu, T., Wu, D., He, B., Wang, W. and Su, C., 2026. Federated continual learning with domain-routed historical expert matching for multimodal perception. Pattern Recognition, p.114532. (SJR-Q1, IF:9.1) [PDF]
  • [C30] Chen, T., Xiang, W., Han, K., Lu, Y., Wu, D., Liu, G. and Kompella, R.R., 2026. GIFSplat: Generative Prior-Guided Iterative Feed-Forward 3D Gaussian Splatting from Sparse Views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 26031-26040). (CORE-A*, CCF-A)[Accept Rate: 25.42% (4090/16092)] [PDF]
  • [C29] Do, M.K., Xiang, W., Han, K., Wu, D., Phan, K., Chen, Y.P.P., Liu, G. and Kompella, R.R., 2026. Spectrally Distilled Representations Aligned with Instruction-Augmented LLMs for Satellite Imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6453-6463). (CORE-A*, CCF-A)[Accept Rate: 25.42% (4090/16092)] [PDF] [CODE]
  • [C28] Li, Y., Chen, W., Zhu, C., Zhang, J., Ge, C., Wu, D. and Long, G., 2026. Meta-FC: Meta-Learning with Feature Consistency for Robust and Generalizable Watermarking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 17420-17429). (CORE-A*, CCF-A)[Accept Rate: 25.42% (4090/16092)] [PDF]
  • [J46] Jia, H., Chen, J., Mao, Q., Wang, Q., Lu, Q., Long, G. and Wu, D.#, 2026. Efficient Image Debiased Contrastive Clustering. IEEE Transactions on Neural Networks and Learning Systems. (SJR-Q1, IF:9.7) [PDF]
  • [J45] Bai, J., Rana, R., Wu, D.#, Qu, Y., Tao, X., Zhang, J., Busso, C. and Palaiahnakote, S., 2026. FedMLAC: Mutual learning driven heterogeneous federated audio classification. Pattern Recognition, p.114250. (SJR-Q1, IF:7.6) [PDF]
  • [J44] Hou, Y., Lv, D., Liwang, M., Wang, J., Li, Z., Xia, X., Wu, D., Tian, Y., Quek, T.Q. and Wang, X., 2026. AsyncFed-SAR: Towards Communication-Efficient Asynchronous Federated Learning for SAR Image Target Recognition. IEEE Transactions on Mobile Computing. (SJR-Q1, CCF-A, IF:8.1) [PDF]
  • [J43] Sui, H., Chen, B., Zhang, J., Zhu, C., Wu, D. and Palaiahnakote, S., 2026. Explanation-guided backdoor defense for ID and OOD attacks in graph neural networks. Pattern Recognition, p.113693. (SJR-Q1, IF:7.6) [PDF]
  • [J42] Yang, W., Wang, S., Li, Y., Wu, D., Zhang, J. and Yang, X., 2026. A lightweight privacy-preserving fingerprint authentication system for IoT devices via pruned and secured minutia cylinder code. Journal of Information Security and Applications, 100, p.104478. (SJR-Q1, IF:3.7) [PDF]
  • [J41] Jafari, P., Ye, G., Wang, J., Chu, T., Hou, B., Wu, D., Huo, S., Wang, H., Sun, Z. and Song, P., 2026. Designing flame retardants for epoxy resins with generative AI for preventing battery thermal runaway propagation. Journal of Materials Science & Technology. (SJR-Q1, IF:14.3) [PDF]
  • [J40] Zou, L., Xu, X., Chen, W., Hong, Q. and Wu, D., 2026. Robust and Stealthy White-Box Watermarking for Intellectual Property Protection of Remote Sensing Object Detection Models. Remote Sensing, 18(7), p.985. (SJR-Q1, IF:5.3) [PDF]
  • [C27] Jia, J., Song, J., Yu, J., Guo, J., Jia, X., Wu, D., Yuan, Y. and Cheng, G., 2026, March. PAGPL: Privacy-Aware Graph Prompt Learning Scheme via Adaptive Perturbation-Estimated Topology Recovery. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (Vol. 40, No. 27, pp. 22247-22255). (CORE-A*, CCF-A)[Accept Rate: 17.6% (4167/23680)] [PDF] [CODE]
  • [C26] Li, K., Li, W., Wu, D.*, Yang, L., Bai, J., Jia, J. and Xue, J., 2026, March. Cross-modal unlearning via influential neuron path editing in multimodal large language models. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (Vol. 40, No. 42, pp. 35589-35597). (CORE-A*, CCF-A)[Accept Rate: 17.6% (4167/23680)] [PDF] [CODE]
  • [C25] Yang, W., Wang, S., Wu, D., Tang, Z., Yang, X., Cui, H., Johnstone, M.N. and Li, Y., 2025, September. Enhancing Privacy in Face Recognition With Dual-Path Feature Compression and Homomorphic Encryption. In 2025 IEEE International Joint Conference on Biometrics (IJCB) (pp. 1-9). IEEE. (CORE-B, CCF-C) [PDF]
  • [J39] Tang, J., Zhang, F., He, X., Nie, S., Ma, X., Ahmed, Z., Chan, N.W., Tan, M.L., Wu, D., Liu, C., and Zhu, Z., 2026. Decoupling hydrodynamic drivers of suspended sediment in hypersaline lakes: A physics-informed machine learning approach. International Journal of Applied Earth Observation and Geoinformation, 148, p.105222. (SJR-Q1, IF:8.6) [PDF]
  • [J38] Hou, Y., Yu, B., Yang, Z., Wang, J., Xiang, W., Wu, D., Liwang, M., Xia, X., Li, Z., Tian, Y. and Sun, Y., 2026. Privacy-Preserving Federated SAR Image Target Recognition with Adaptive Resource Management in Space-Air-Ground Integrated Networks. Pattern Recognition, p.113253. (SJR-Q1, CCF-B, IF:7.6) [PDF]
  • [J37] Schmierer, T., Li, T., Wu, D. and Li, Y., 2026. Prediction of Anesthesia Depth and Early Detection of State Transitions Using EEG-derived Features. IEEE Transactions on Instrumentation and Measurement. (SJR-Q1, IF:5.9) [PDF]
  • [J36] Yang, W., Wu, D.*, Bai, J., Wang, J., Rong, S. and Zhou, J., 2026. FedDA-HSI: Federated Class-Aware Framework for Hyperspectral Image Classification with Diffusion Augmentation. IEEE Transactions on Geoscience and Remote Sensing. (SJR-Q1, CCF-B, IF:8.6) [PDF]
  • [J35] Hou, Y., Zhao, S., Xia, X., Liwang, M., Li, Z., Xu, N., Wu, D., and Quek, T., 2026. FedC-DAC: A Federated Clustering with Dynamic Aggregation and Calibration Method for SAR Image Target Recognition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. (SJR-Q1, IF:5.3) [PDF]
  • [J34] Liao, X., Wu, Z., He, B., Zhao, H., Shang, S., Wu, D.# and Su, C., 2025. Dynamic Generator: A Stealth-Preserving Generator-Based Data Poisoning Attack in Federated Learning with Defensive Countermeasures in IoT Systems. IEEE Internet of Things Journal. (SJR-Q1, CCF-C, IF:8.2) [PDF]
  • [J33] Chen, W., Li, Y., Zhang, J., Ge, C., Wu, D., Susilo, W. and Palaiahnakote, S., 2025. GResMark: A Swin Transformer-based Watermarking Framework with Geometric Attack Resilience. Expert Systems with Applications, p.130807. (SJR-Q1, CCF-C, IF:7.5) [PDF]
  • [J32] Zhu, L., Zhang, X., Zhang, Z., Wu, D. and Gao, L, 2026. LegiCode: A blockchain-legal LLM framework for real-time compliance in smart contract generation. Empirical Software Engineering 31, 24 (SJR-Q1, CCF-B, IF:3.6) [PDF]
  • [J31] Yang, J., Li, P., Yun, X., Zhong, X. and Wu, D.#, 2025. Machine Unlearning for Source-Free Unsupervised Partial-Domain Adaptation in Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-12. (SJR-Q1, CCF-B, IF:8.6) [PDF]
  • [J30] Ma, X., Yin, Z., Cao, Z., Shi, B., Yan, X., Wu, D., Wang, Y., Dai, C. and Hong, M., 2025. Machine learning assisted photocurrent wavelength classification in a self-powered broadband photoelectrochemical photodetector based on Bi2Te0.6Se2.4/MoSe2 heterojunctions. Chemical Engineering Journal, p.169645. (SJR-Q1, IF:13.2) [PDF]
  • [J29] Huang, L., Mao, X., Zhou, L., Wu, D.#, Gao, L. and Luan, T.H., 2025. FiDD: Secure Fine-Grained Deduplication and Dynamic Auditing Scheme for Cloud Storage. IEEE Transactions on Networking. (SJR-Q1, CCF-A, IF:3.6) [PDF]
  • [J28] Li, Y., Li, J., Yuan, C., Wu, D.#, Liu, S., Zhou, Z., Wu, C. and Ling, L., 2025. TurboPSD prediction framework: Utilizing multivariable temporal dynamics for precise grinding production PSD (particle size distribution) estimates. Powder Technology, p.121659. (SJR-Q1, IF:4.6) [PDF]
  • [J27] Tang, J., Zhang, F., Chan, N.W., Johnson, V.C., Wu, D., Wei, L., Tan, M.L., Ma, X., Feng, Z. and Li, Z., 2025. Water color remote sensing models: Inversion mechanisms, optimization, and challenges. Journal of Sea Research, p.102622. (SJR-Q1, IF:2.9) [PDF]
  • [J26] Cao, S., Sun, X., Widyasari, R., Lo, D., Wu, X., Bo, L., Zhang, J., Li, B., Liu, W., Wu, D. and Chen, Y., 2025. A Systematic Literature Review on Explainability for ML/DL-based Software Engineering. ACM Computing Surveys. (SJR-Q1, IF:28.0) [PDF]
  • [J25] Feng, Z., Zhang, F., Cai, X., Liu, C., Wu, D., Zhu, Z., Ahmed, Z., Yuan, S., Ma, X., Wei, L. and He, X., 2025. Quantitative inversion of suspended particulate matter in Ebinur lake, an arid region: Evidence based on radiative transfer mechanism model. Journal of Hydrology: Regional Studies, 61, p.102714. (SJR-Q1, IF:5.3) [PDF]
  • [J24] Feng, Z., Zhang, F., Ma, X., Jim, C.Y., Wu, D., Liu, D., Oke, S.A., Wang, W., Wei, L., Nie, S. and Li, Z., 2025. Refining Water Body Extraction by Remote Sensing with Deep Learning Models: Exploring Different Band Combinations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. (SJR-Q1, IF:5.3) [PDF]
  • [J23] Sun, X., Zhou, M., Cao, S., Wu, X., Bo, L., Wu, D., Li, B. and Xiang, Y., 2025. HgtJIT: Just-in-Time Vulnerability Detection Based on Heterogeneous Graph Transformer. IEEE Transactions on Dependable and Secure Computing. (SJR-Q1, CCF-A, IF:7.5) [PDF] [CODE]
  • [C24] Bai, J.*, Song, Y.*, Wu, D.*, Sajjanhar, A., Xiang, Y., Zhou, W., Tao, X., Li, Y. and Li, Y., 2025, August. A unified solution to diverse heterogeneities in one-shot federated learning. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (KDD) (pp. 71-82). (CORE-A*, CCF-A)[Accept Rate: 14% (1632/11442)] [PDF] [CODE]
  • [C23] Jia, J., Yu, J., Wu, D.#, Wu, C., Zhu, H. and Wang, L., 2025, August. Prompt as a Double-Edged Sword: A Dynamic Equilibrium Gradient-Assigned Attack against Graph Prompt Learning. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2 (KDD) (pp. 1049-1060). (CORE-A*, CCF-A)[Accept Rate: 14% (1632/11442)] [PDF]
  • [C22] Gao, X., Sun, X., Cao, S., Huang, K., Wu, D., Liu, X., Lin, X., Xiang, Y., 2025. MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem. In Proceedings of the Thirty-Forth Usenix Security Symposium (Usenix Security). (CORE-A*, CCF-A)[Accept Rate: 17.1% (407/2385) ] [PDF]
  • [J22] Li, Y., Liu, R., Ji, Z., Gao, L., Wang, X., Zhang, J., Hu, L., Qu, Y., Bai, J., Wu, D.# and Han, S., 2025. Predicting lymphatic transport potential using graph transformer based on limited historical data from in vivo studies. Journal of Controlled Release, p.113847. (SJR-Q1, IF:10.5) [PDF]
  • [J21] Sui, H., Zhang, J., Chen, B., Wu, D., Sun, X. and Palaiahnakote, S., 2025. EPAD: Ethereum phishing scam detection via graph contrastive learning. Expert Systems with Applications, p.128227. (SJR-Q1, CCF-C, IF:7.5) [PDF]
  • [J20] Yang, W., Wang, S., Wu, D., Cai, T., Zhu, Y., Wei, S., Zhang, Y., Yang, X., Tang, Z. and Li, Y., 2025. Deep learning model inversion attacks and defenses: a comprehensive survey. Artificial Intelligence Review, 58(8), pp.1-52. (SJR-Q1, IF:10.7) [PDF]
  • [J19] Chen, W., Zhang, W., Wu, D.#, Keskinarkaus, A., Seppänen, T., Zhang, J., Gao, L. and Luan, T.H., 2025. FedMLC: White-box Model Watermarking for Copyright Protection in Federated Learning for IoT Environment. IEEE Internet of Things Journal. (SJR-Q1, CCF-C, IF:8.2) [PDF]
  • [J18] Zheng, X., Li, H., Wu, D., Bu, T., Hu, P. and Zhang, J., 2025. Federated Average Clustering Learning Based on Time-Asynchronous Similarity. IEEE Transactions on Big Data. (SJR-Q1, CCF-C, IF:7.5) [PDF]
  • [C21] Zhang, J.*, Li, H.*, Wu, D.*, Sun, X., Lu, Q. and Long, G., 2025, April. Beyond Dataset Watermarking: Model-Level Copyright Protection for Code Summarization Models. In Proceedings of the ACM on Web Conference (WWW) (pp. 147-157). (CORE-A*, CCF-A)[Accept Rate: 19.8% (409/2062)] [PDF] [CODE]
  • [J17] Bai, J., Wu, D., Shelley, T., Schubel, P., Twine, D., Russell, J., Zeng, X., Zhang, J., 2025. A Comprehensive Survey on Machine Learning Driven Material Defect Detection. ACM Computing Surveys. (SJR-Q1, IF:23.8) [PDF]
  • [C20] Liu, Y., Xing, L., Wang, D., Nie, X., Zhang, X., Wu, D. and Liu, W., 2024, August. An Early Detection Model of Prostate Cancer Based on MRI. In IEEE 9th International Conference on Data Science in Cyberspace (DSC) (pp. 337-344). IEEE. [PDF]
  • [J16] Schmierer, T., Li, T., Wu, D. and Li, Y., 2025. Advancing DoA assessment through federated learning: A one-shot pseudo data approach. Neurocomputing, 634, 129812. (SJR-Q1, IF:5.5) [PDF] [CODE]
  • [J15] Bai, J., Wu, D.#, Zeng, S., Zhao, Y., Qu, Y. and Yu, S., 2025. Non-IID Free Federated Learning with Fuzzy Optimization for Consumer Electronics Systems. IEEE Transactions on Consumer Electronics. (SJR-Q1, IF:5.5) [PDF]
  • [J14] Zhang, J., Li, Y., Wu, D., Zhao, Y. and Palaiahnakote, S., 2025. SFFL: Self-Aware Fairness Federated Learning Framework for Heterogeneous Data Distributions. Expert Systems with Applications, p.126418. (SJR-Q1, CCF-C, IF:7.5) [PDF]
  • [J13] Zuo, W., Gao, X., Wu, D., Liu, J., Zeng, X. and Li, C., 2025. YOLO-SCNet: A Framework for Enhanced Detection of Small Lunar Craters. Remote Sensing, 17(11), p.1959. (SJR-Q1, IF:4.2) [PDF]
  • [C19] Zhang, J.*, Zhu, C.*, Wu, D.*, Sun, X, Yong, J. and Long, G., 2024. BADFSS: Backdoor Attacks on Federated Self-Supervised Learning. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI). (CORE-A*, CCF-A)[Accept Rate: 14.0% (791/5651)] [PDF]
  • [J12] Cai, B., Xiang, Y., Gao, L., Wu, D., Zhang, H., Jin, J. and Luan, T., 2024. From wide to deep: dimension lifting network for parameter-efficient knowledge graph embedding. IEEE Transactions on Knowledge and Data Engineering. (SJR-Q1, CCF-A, IF:8.9) [PDF]
  • [J11] Zhang, K., Zhao, X., Chen, Y., Wu, D., Cai, T., Wang, Y., Li, L. and Zhang, J., 2024. Robust equivalent circuit model parameters identification scheme for State of Charge (SOC) estimation based on maximum correntropy criterion. International Journal of Electrochemical Science, 19(5), p.100558. (SJR-Q4, IF: 1.541) [PDF]
  • [J10] 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]
  • [J9] 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]
  • [C18] Wu, D., Bai, J., Song, Y., Chen, J., Zhou, W., Xiang, Y. and Sajjanhar, A., 2024, October. FedInverse: Evaluating Privacy Leakage in Federated Learning. In The Twelfth International Conference on Learning Representations (ICLR). (CORE-A*)[Accept Rate: 30.52% (2260/7404)] [PDF] [CODE]
  • [C17] Guo, Q., Shang, Q., Chen, J., Fu, Z., Long, T. and Wu, D., 2023, November. Campus Wireless Network Operation Situation Assessment Based on Spatial Location. In 2023 3rd International Conference on Networking Systems of AI (INSAI) (pp. 271-274). IEEE. [PDF]
  • [J8] 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]
  • [J7] 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]
  • [J6] 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]
  • [C16] 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]
  • [J5] 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]
  • [C15] 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]
  • [C14] 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]
  • [J4] 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]
  • [C13] 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]
  • [C12] 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]
  • [C11] 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]
  • [C10] 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]
  • [C9] 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]
  • [J3] 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]
  • [C8] 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]
  • [C7] 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-A, CCF-C) [PDF]
  • [C6] 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-A, CCF-C) [PDF]
  • [C5] 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]
  • [C4] 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]
  • [J2] 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]
  • [C3] 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-A, CCF-C) [PDF]
  • [J1] 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]
  • [C2] 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]
  • [C1] 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]
  • Wu, D., 2019. Video-based similar gesture action recognition using deep learning and GAN-based approaches (Doctoral dissertation). [PDF]
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