Baihe Ma

Optimistic and passionate professional with extensive research, publication, project execution, supervision, and teaching experience. Skilled in project reporting, communication, and teamwork; capable of independently completing projects. Proficient as a peer reviewer for high-quality academic papers.

• VTS NSW student contest 2021, third prize
• VTS NSW student contest 2022, ranked forth
• VTS NSW student contest 2023, third prize
• Unite 2022 conference, presenter for VTS talks on Trusted and Connected Intelligence
• Full-scholarship in Ph.D.
• Full-scholarship in Master’s degree
• The excellent graduate students in Master’s degree, 2018
• Selection Program of Excellent Scientific and Research Talents, 2016

• Ma, Baihe, et al. “Personalized Location Privacy with Road Network-Indistinguishability.” IEEE Transactions on Intelligent Transportation Systems 23.11 (2022): 20860-20872., Ma, Baihe, et al. “New Cloaking Region Obfuscation for Road Network-Indistinguishability and Location Privacy.” Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses. 2022., Ma, Baihe, et al. “Vehicle Trajectory Obfuscation and Detection.” Cybersecurity for Smart Cities. Springer, Cham, 2023. 121-134., Ma, Baihe, et al. “Differential Privacy-based Location Privacy Protection with Hilbert Curve in Vehicular Networks,” Proceedings of the 2nd International Conference on Network Simulation and Evaluation. 2023., Ma, Baihe, et al. "Cooperative jamming for secrecy of wireless communications." 2018 International Conference on Networking and Network Applications (NaNA). IEEE, 2018., Ma, Baihe, et al. "Cooperative Jamming for Secrecy of Wireless Communications." Journal of Information Science and Engineering 35.5 (2019): 1029-1044., Ma, Baihe, et al. "A summary of traffic identification method depended on machine learning." 2018 International Conference on Sensor Networks and Signal Processing (SNSP). IEEE, 2018., Jiang, Y.
• And Ma, Baihe
• Et al. A Secure Aggregation for Federated Learning on Long-Tailed Data. Accepted by 2022 Conference on Neural Information Processing Systems (NeurIPS) Workshop., Zhao, Y.
• Et al. Trajectory Obfuscation and Detection in Internet-ofvehicles. 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2022., Wang, Z.
• Et al. Differential Preserving in XGBoost Model for Encrypted Traffic Classification. 2022 International Conference on Networking and Network Applications (NaNA). IEEE, 2022., Shi, K.
• Et al. Layered classification method for darknet traffic based on Weighted K-NN. 2022 International Conference on Networking and Network Applications (NaNA). IEEE, 2022., Guo, X.
• Et al. Fine-Grained Defense Methods in Encrypted Traffic Inspection." The 15th China Computer Networks and Information Security Conference & 2022 International Cyberspace Security Academic Summit Forum. 2022., Guo, X.
• Et al. Fine-Grained Defense Methods in Federated Encrypted Traffic Inspection". Accepted by Journal of Xidian University. 132, Wang, Z.
• Et al. 2023, DLPriv: Deep Learning based Dynamic Location Privacy Mechanism for LBS in Internet-of-Vehicles. Accepted by 2023 International Conference on Networking and Network Applications (NaNA2023)., Lin, X., Ma, Baihe, Wang, X., He, Y., Liu, R.P. and Ni, W., 2022, May. Multi-layer reverse engineering system for vehicular controller area network messages. In 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp.1185-1190). IEEE., Liang, L., Lin, X., Ma, Baihe, Wang, X., He, Y., Liu, R.P. and Ni, W., 2022, December. Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks. In GLOBECOM 2022-2022 IEEE Global Communications Conference (pp. 4903-4908). IEEE., Shi, K., Zeng, Y., Ma, Baihe, Liu, Z., and Ma, J., 2023, MT-CNN: A Classification Method of Encrypted Traffic Based on Semi-Supervised Learning. Accepted 2023 IEEE Global Communications Conference: Communication & Information Systems Security. IEEE., Lin, X., Ai, D., Ma, Baihe, Wang, X., Yu, G., He, Y., Ni, W., and Liu, R., “Federated Learning-based Intrusion Detection System for In-Vehicle Network Using Statistics of Controller Area Network Messages.” Proceedings of the 2nd International Conference on Network Simulation and Evaluation. 2023., Zhang, H., Ma, Baihe, Guo, Y., Liu, Z., and Zeng, Y., "An Efficient Authentication Scheme for Privacy-Preserving in Secure Vehicular Communications." 2018 International Conference on Sensor Networks and Signal Processing (SNSP). IEEE, 2018., A social network protection method based on degree anonymity and link perturbation, Publication No.: CN109800573A, Date of Publication: 2019.05.24, A snoop-proof key entry system and method, Publication No.: CN108229217A, Date of Publication: 2018.06.29, Vehicle Coexistence Authentication Method Based on Trajectory Information, Publication No.: CN109587638A, Date of Publication: 2019.04.05, Ma, Baihe et al., "Unlinkable Obfuscation Mechanism for Road Network-Indistinguishability and Location Privacy." submitted to CCS., Ma, Baihe et al., "Location Privacy Threats and Protections in Future Vehicular Networks: A Comprehensive Review." submitted to IEEE Communications Surveys and Tutorials, second round review., Ma, Baihe et al., "Achieve Both Anonymity and Privacy: Differential Privacy is All You Need." submitted to CCS., Ma, Baihe et al., “Privacy in Vehicular Networks: Challenges and Solutions.” Book, under review of CRC Press., Ma, Baihe et al., “Adaptive Cloaking Region Obfuscation in Road Networks.” Invited paper at the 3rd Conference on Fully Actuated System Theory and Applications., Jiang, Y.
• Et al., "Blockchained Federated Learning for Internet of Things: A Comprehensive Survey." submitted to ACM Computing Surveys. Second round review., Tan, S., Wang, C., Shen, Y., Qian, C., Ma, Baihe, and Gao, J., "Dynamic Model for Umbrella Antenna Pointing Performance and Sensitivity Considering Inter-rib Tension Rope Stiffness Uncertainty." submitted to Journal of Aerospace Engineering., Jiang, Y.
• Et al., "A Novel Reconstruction Attack on Differentially Private Multi-Attributed Data by Exploiting Attribute Correlation." submitted to Transactions on Intelligent Systems and Technology., Lin, X.
• Et al., "CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories." submitted to IEEE Transactions on Intelligent Transportation Systems., Lin, X
• Et al., “ByCAN: Reverse Engineering Controller Area Network (CAN) Messages from Bit to Byte Level.” submitted to IEEE Internet of Things Journal.

• Prof. Ren Ping Liu, https://www.uts.edu.au/staff/renping.liu, RenPing.Liu@uts.edu.au
• Prof. Wei Ni, https://people.csiro.au/N/W/Wei-Ni, Wei.Ni@data61.csiro.au
• Dr. Xu Wang, https://profiles.uts.edu.au/Xu.Wang-1, Xu.Wang-1@uts.edu.au
• Prof. Zhihong Liu, https://faculty.xidian.edu.cn/LZH3, liuzhihong@mail.xidian.edu.cn