Yunpeng Luo (UC Irvine), Ningfei Wang (UC Irvine), Bo Yu (PerceptIn), Shaoshan Liu (PerceptIn) and Qi Alfred Chen (UC Irvine)

Autonomous Driving (AD) is a rapidly developing technology and its security issues have been studied by various recent research works. With the growing interest and investment in leveraging intelligent infrastructure support for practical AD, AD system may have new opportunities to defend against existing AD attacks. In this paper, we are the first t o systematically explore such a new AD security design space leveraging emerging infrastructure-side support, which we call Infrastructure-Aided Autonomous Driving Defense (I-A2D2). We first taxonomize existing AD attacks based on infrastructure-side capabilities, and then analyze potential I-A2D2 design opportunities and requirements. We further discuss the potential design challenges for these I-A2D2 design directions to be effective in practice.

View More Papers

Detecting Obfuscated Function Clones in Binaries using Machine Learning

Michael Pucher (University of Vienna), Christian Kudera (SBA Research), Georg Merzdovnik (SBA Research)

Read More

Get a Model! Model Hijacking Attack Against Machine Learning...

Ahmed Salem (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yang Zhang (CISPA Helmholtz Center for Information Security)

Read More

datAFLow: Towards a Data-Flow-Guided Fuzzer

Adrian Herrera (Australian National University), Mathias Payer (EPFL), Antony Hosking (Australian National University)

Read More

SemperFi: Anti-spoofing GPS Receiver for UAVs

Harshad Sathaye (Northeastern University), Gerald LaMountain (Northeastern University), Pau Closas (Northeastern University), Aanjhan Ranganathan (Northeastern University)

Read More