Much security research focuses on protecting the digitalized information, e g , securing communication via cryptographic methods Nevertheless, hardware implementation and its internal signal conditioning path could undermine the otherwise secure mechanisms, e g , attackers can extract secret keys via side channels As the emerging cyber-physical systems depend on sensors to make automated decisions, it is critical to examine analog cybersecurity, i e , analyzing the integrity and dependability of information prior to its digitalization Such a problem is especially important in cyber-physical systems because they depend on sensors to make automated decisions In this talk, we illustrate a few analog signal injection attacks that utilize the build-in hardware vulnerabilities of various commodity sensing systems as well as proposing the defense strategies Our work calls into questioning the wisdom of allowing microprocessors and embedded systems to blindly trust that hardware abstractions alone will ensure the integrity of sensor outputs
Wenyuan Xu is currently a professor in the College of Electrical Engineering at Zhejiang University. She received her B.S. degree in Electrical Engineering from Zhejiang University in 1998, an M.S. degree in Computer Science and Engineering from Zhejiang University in 2001, and the Ph.D. degree in Electrical and Computer Engineering from Rutgers University in 2007. Her research interests include wireless networking, embedded systems security, and IoT security. Dr. Xu received the NSF Career Award in 2009 and was selected as a young professional of the thousand talents plan inChinain 2012. She was granted tenure (an associate professor) in the Department of Computer Science and Engineering at the University of South Carolina in the U.S. She has served on the technical program committees for several IEEE/ACM conferences on wireless networking and security, and she is an associated editor of EURASIP Journal on Information Security. She has published over 60 papers and her papers have been cited over 3000 times (Google Scholar).