报告题目：Approaches to Secure Inference in the Internet of Things
报告摘要：The Internet of Things (IoT) improves pervasive sensing and control capabilities via the aid of modern digital communication, signal processing and massive deployment of sensors. The employment of low-cost and spatially distributed IoT sensor nodes with limited hardware and battery power, along with the low required latency to avoid unstable control loops, presents severe security challenges. In this talk, some signal processing-based approaches to secure inference in the IoT in the presence of different types of cyberattacks are presented. Specifically, estimation of an unknown deterministic vector from quantized IoT sensor data is considered in the presence of spoofing attacks and man-in-the-middle attacks which alter the data presented to several sensors. First, asymptotically optimum processing, which identifies and categorizes the attacked sensors into different groups according to distinct types of attacks, is outlined in the face of man- in-the-middle attacks. Necessary and sufficient conditions are provided under which utilizing the attacked sensor data will lead to better estimation performance when compared to approaches where the attacked sensors are ignored. Next, necessary and sufficient conditions are provided under which spoofing attacks provide a guaranteed attack performance in terms of the Cramer-Rao Bound (CRB) regardless of the processing the estimation system employs, thus defining a highly desirable attack. Interestingly, these conditions imply that, for any such attack when the attacked sensors can be perfectly identified by the estimation system, either the Fisher Information Matrix (FIM) for jointly estimating the desired and attack parameters is singular or the attacked system is unable to improve the CRB for the desired vector parameter through this joint estimation even though the joint FIM is nonsingular. It is shown that it is always possible to construct such a highly desirable attack by properly employing an attack vector parameter having a sufficiently large dimension relative to the number of quantization levels employed, which was not observed previously.
Jiangfan Zhangis currently an Assistant Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology, Rolla, MO, USA. From 2016 to 2018, he was a Postdoctoral Research Scientist with the Department of Electrical Engineering, Columbia University in the City of New York, New York, NY, USA. He received the B.Eng. degree in communication engineering from Huazhong University of Science and Technology, Wuhan, China, in 2008, the M.Eng. degree in information and communication engineering from Zhejiang University, Hangzhou, China, in 2011, and the Ph.D. degree in electrical engineering from Lehigh University, Bethlehem, PA, USA, in 2016. Dr. Zhang is a recipient of the Dean’s Doctoral Student Fellowship, Gotshall Fellowship, and a P. C. Rossin Doct oral Fellow at Lehigh University. His current research interests span the general area of signal processing,communications, data science, machine learning, and AI-driven technologies, as well as their cross-disciplinary applications to cybersecurity, Internet of Things, sensor networking, cyber-physical systems, smart grid, and underwater acoustic signal processing.