Publications
Y. Ma, Y. Wu, and J. Ge, Accountability and Privacy in Network Security, Springer, ISBN: 978-981-15-6574-8, 2020. (Monograph)
J. Qu, F. Liu, and Y. Ma, “A dual encoder DAE neural network for imbalanced binary classification based on NSGA-III and GAN,”. Pattern Analysis and Applications, vol. 25, p. 17–34, 2022. (SCI,IF: 2.58) Paper
L. Zhang, X. Chen, and Y. Ma, “A Handover Scheme Based on Mobility Prediction for Autonomous Moving Platforms in 5G Networks,” in International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2022, pp. 104-109. (EI)
Y. Wu, Z. Wang, Y. Ma, and V. Leung, “Deep reinforcement learning for blockchain in industrial IoT: A survey,” Computer Networks, vol. 191, p. 108004, 2021. (SCI,IF: 3.111) Paper
Y. Wu, Y. Ma, H.-N. Dai, and H. Wang, “Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks,” Computer Networks, vol. 185, p. 107743, 2021. (SCI,IF: 3.111) Paper
L. Zhang, Y. Huo, Q. Ge, Y. Ma, Q. Liu, and W. Ouyang, “A Privacy Protection Scheme for IoT Big Data Based on Time and Frequency Limitation,” Wireless Communications and Mobile Computing, vol. 2021, 5545648, 2021. (SCI,IF:2.336) Paper
Y. Ma, X. Chen, and L. Zhang, “Base Station handover Based on User Trajectory Prediction in 5G Networks,” in IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2021, pp. 1476-1482. (EI, CCF)
Y. Liu, Y. Ma, J. Gao, Z. Zhao, and J. Li, “Coupled Self-Exciting Process for Information Diffusion Prediction Coupled Self-Exciting Process for Information Diffusion Prediction,” in IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2021, pp. 1394-1401. (EI, CCF)
Y. Ma, Y. Wu, J. Li, and J. Ge, “APCN: A Scalable Architecture for Balancing Accountability and Privacy in Large-Scale Content-Based Networks,” Information Sciences, vol. 527, pp. 511-532, 2020. (SCI,IF: 5.524) Paper
Z. Yao, J. Ge, Y. Wu, X. Lin, R. He, and Y. Ma, “Encrypted traffic classification based on Gaussian mixture models and Hidden Markov Models,” Journal of Network and Computer Applications, vol. 166, pp. 102711, 2020. (SCI,IF: 5.273) Paper
J. Qu, F. Liu, Y. Ma, and J. Fan, “A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery,” IEEE Access, vol. 7, pp. 87178–87191, 2019. (SCI,IF: 4.098) Paper
Y. Ma, Y. Wu, J. Ge, and J. Li, “An Architecture for Accountable Anonymous Access in the Internet-of-Things Network,” IEEE Access, vol. 6, pp. 14451–14461, 2018. (SCI,IF: 4.098) Paper
Y. Ma, Y. Wu, J. Ge, and J. Li, “A Flow-Level Architecture for Balancing Accountability and Privacy,” in IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2018, pp. 984–989. (EI, CCF)
Y. Ma, Y. Wu, J. Li, and J. Ge, “A New Architecture for Distributed Computing in Named Data Networking,” in IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2018, pp. 474–479. (EI, CCF)
Y. Ma, Y. Wu, J. Ge, and J. Li, “A New Architecture for Anonymous Use of Services in Distributed Computing Networks,” in IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2017, pp. 368–374. (EI, CCF)
董谦, 李俊, 马宇翔, 韩淑君. 软件定义网络中基于分段路由的流量调度方法[J]. 通信学报, 2018, 39(11): 23-35. (EI) Paper
董谦, 李俊, 马宇翔. 基于集中控制的命名数据网络流量调度方法[J]. 通信学报, 2018, 39(7): 68-80. (EI) Paper