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智能计算研究所前沿报告系列十三

发布时间:2021-05-20 编辑:徐慧 来源:

 

Title: Hardware based Lightweight Authentication for IoT Applications

Speaker: Prof. Gang Qu

Time: 2021/5/21 09:00-10:00

Place: ZoomID947 5901 4251 Code568851

 

Abstract:

In many embedded systems and the Internet of Things (IoT) applications, resources like CPU, memory, and battery power are so limited that they cannot afford the classic cryptographic security solutions. Meanwhile, the security requirement on these systems/devices is not as high as the traditional secure systems. This creates the challenge and opportunity for the study of lightweight security on resource constrained systems. In this talk, we use authentication as an example to demonstrate how hardware and system physical characteristics can help to build lightweight security primitives. More specifically, after a brief introduction of IoT system's demand of authentication and the circuit fingerprinting techniques for chip identification, we will focus on how to elevate the fabrication variation during chip manufacturing process to high level applications for authentication purposes. We will report our recent work on image processing under voltage over scaling for device authentication, GPS spoofing detection and survival, and machine learning model inversion attack defense. These practical approaches are promising alternatives for the classical crypto-based authentication protocols for the embedded and IoT devices in the smart world.

 

Bio:

Gang Qu received his B.S. in mathematics from the University of Science and Technology of China (USTC) and Ph.D. in computer science from the University of California, Los Angeles (UCLA). He is currently a professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park, where he leads the Maryland Embedded Systems and Hardware Security Lab (MeshSec) and the Wireless Sensor Laboratory. His research activities are on trusted integrated circuit design, hardware security, energy efficient system design and wireless sensor networks. He has focused recently on applications in the Internet of Things, cyber-physical systems, and machine learning. He has published more than 250 conference papers and journal articles on these topics with several best paper awards. Dr. Qu is an enthusiastic teacher. He has taught and co-taught various security courses, including a popular MOOC on Hardware Security through Coursera. Dr. Qu has served 17 times as the general or program chair/co-chair for international conferences and workshops. He is currently on the editorial board of IEEE TCAD, TETC, ACM TODAES,  JCST, Integration, and HSS. Dr. Qu is a fellow of IEEE.  

 

 

 

 

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