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Guyue Li, Southeast University, China |
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. 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。
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