ICCT 2022 Invited Speaker

Guyue Li, Southeast University, China
李古月, 东南大学

Guyue Li is currently an Associate Professor with the School of Cyber Science and Engineering, Southeast University. She is an IEEE Member and a senior Member of China Institute of Communications. She was honored the Zhishan Scholar of Southeast University and the Excellent Instructor of the "14th National College Student Information Security Competition". Dr. Li received the Ph.D. degree in information security from School of Information Science and Engineering, Southeast University, Nanjing, China in 2017. She has visited Tampere University of Technology, Finland and ESIEE, Paris in 2014 and 2019, respectively. Her research interests include physical-layer security and IoT security. She has published more than 50 papers on the top journals/conferences and 12 granted patents in the related fields. She is the PI of two NSFC project and Co-PI of National Key Research and Development Program. The proposed technologies, including radio frequency fingerprint and secret key generation from wireless channels have been verified and applied in the smart grid IoT network, Quantum IoT network and vehicle networks.
李古月，东南大学网络空间安全学院副教授，IEEE会员，通信学会高级会员，荣获东南大学“至善学者”及“第十四届全国大学生信息安全竞赛”优秀指导教师。2017年东南大学信息科学与工程学院博士毕业，2014年赴芬兰坦佩雷理工访学，2019年赴巴黎高等电子与电工技术工程师学校访问。主要研究方向为物理层安全、物联网安全等，在国际顶级会议/期刊上发表论文50余篇，授权发明专利12项。主持两项国家自然科学基金项目，并参与国家重点研发计划等项目，提出的射频指纹识别、无线信道密钥等技术在电力物联网、量子物联网与车联网等领域得到应用验证。

报告题目：基于混合信号指纹的车载以太网设备识别方法

随着汽车电动化进程的加速推进，车内数据传输需求快速增长。车载以太网因为其本身具有的开放性，互联扩展等优势，目前已成为智能汽车内高速传输的骨干技术之一。然而，近年来层出不穷的恶意攻击也对车载以太网的安全构成了严重威胁。一旦恶意设备被接入车载网络，将造成难以预计的人生安全威胁及财产损失。针对该问题，本次报告将介绍一种基于混合信号指纹的车载以太网设备识别方法，可以从物理层检测出恶意设备，抵御设备被更换的风险。该方法利用了车载以太网物理层的数据加扰机制以其特有的pam3编码方式，提取的功率谱指纹特征与发送数据无关并适用于双端混合信号，可以很好地应用于车载以太网设备的识别，且具备一定的鲁棒性。实测结果表明，所提方法在10个车载以太网设备上的识别成功率可以达到95%以上。