“Securing Robotic Vehicles : A Cross-Layer Approach”
Chung Hwan Kim is an Assistant Professor of Computer Science at the University of Texas at Dallas (UT Dallas). Before joining UT Dallas in 2020, he received his Ph.D. in Computer Science from Purdue University in 2017 and worked at NEC Labs as a Researcher for three years. His research interest lies in solving security and reliability problems in modern computing systems, recently with more focus on the security and safety of cyber-physical systems. His research seeks to achieve this by using and developing a unique combination of techniques in program analysis and operating systems. His work has been nominated as a top 10 finalist for the CSAW Best Applied Research Paper Award (2018) and received a UT Dallas New Faculty Research Symposium Grant award (2021). He has served as a program committee member of NDSS’19 and DSN’20, and he is currently serving in the program committee of ICDCS’21 and organizing committee of IEEE SecDev’21.
Robotic vehicles (RVs) such as unmanned aerial vehicles are a type of cyber-physical system for autonomous transportation and missions. With their increasing adoption, RVs are facing threats of cyber and cyber-physical attacks that exploit their attack surfaces. Although many RVs are critical to human safety and the environment, it is difficult to make them secure against such attacks due to new challenges that are not addressable by traditional approaches. Many of these challenges originate from the limited hardware resources and semantic unawareness of RV controllers in security mechanisms.In this seminar, we will discuss my recent work that discovers and removes two new attack surfaces in RVs. More specifically, I will first present an attack surface caused by the absence of memory isolation in RVs. I will show how a new system architecture of RV controller eliminates more than 75% of the attack surface under hardware and real-time constraints. We will then discuss a new type of semantic vulnerabilities in RV controllers and a novel tool to detect the vulnerabilities in commodity RVs through automated testing.
ZOOM : https://zoom.us/j/8978217407?pwd=d1pOZmF1OWlseEdZRVBpV3VuSkl3dz09
ID : 897 821 7407
PW : 1nTQDY