Abdullah Zubair Mohammed (Virginia Tech), Yanmao Man (University of Arizona), Ryan Gerdes (Virginia Tech), Ming Li (University of Arizona) and Z. Berkay Celik (Purdue University)

The Controller Area Network (CAN) bus standard is the most common in-vehicle network that provides communication between Electronic Control Units (ECUs). CAN messages lack authentication and data integrity protection mechanisms and hence are vulnerable to attacks, such as impersonation and data injection, at the digital level. The physical layer of the bus allows for a one-way change of a given bit to accommodate prioritization; viz. a recessive bit (1) may be changed to a dominant one (0). In this paper, we propose a physical-layer data manipulation attack wherein multiple compromised ECUs collude to cause 0→1 (i.e., dominant to recessive) bit-flips, allowing for arbitrary bit-flips in transmitted messages. The attack is carried out by inducing transient voltages in the CAN bus that are heightened due to the parasitic reactance of the bus and non-ideal properties of the line drivers. Simulation results indicate that, with more than eight compromised ECUs, an attacker can induce a sufficient voltage drop to cause dominant bits to be flipped to recessive ones.

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Demo #14: In-Vehicle Communication Using Named Data Networking

Zachariah Threet (Tennessee Tech), Christos Papadopoulos (University of Memphis), Proyash Poddar (Florida International University), Alex Afanasyev (Florida International University), William Lambert (Tennessee Tech), Haley Burnell (Tennessee Tech), Sheikh Ghafoor (Tennessee Tech) and Susmit Shannigrahi (Tennessee Tech)

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Hiding My Real Self! Protecting Intellectual Property in Additive...

Sizhuang Liang (Georgia Institute of Technology), Saman Zonouz (Rutgers University), Raheem Beyah (Georgia Institute of Technology)

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Model-Agnostic Defense for Lane Detection against Adversarial Attack

Henry Xu, An Ju, and David Wagner (UC Berkeley) Baidu Security Auto-Driving Security Award Winner ($1000 cash prize)!

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Demo #7: Automated Tracking System For LiDAR Spoofing Attacks...

Yulong Cao, Jiaxiang Ma, Kevin Fu (University of Michigan), Sara Rampazzi (University of Florida), and Z. Morley Mao (University of Michigan) Best Demo Award Runner-up ($200 cash prize)!

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