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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Let’s Authenticate: Automated Certificates for User Authentication

James Conners (Brigham Young University), Corey Devenport (Brigham Young University), Stephen Derbidge (Brigham Young University), Natalie Farnsworth (Brigham Young University), Kyler Gates (Brigham Young University), Stephen Lambert (Brigham Young University), Christopher McClain (Brigham Young University), Parker Nichols (Brigham Young University), Daniel Zappala (Brigham Young University)

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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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P4DDPI: Securing P4-Programmable Data Plane Networks via DNS Deep...

Ali AlSabeh (University of South Carolina), Elie Kfoury (University of South Carolina), Jorge Crichigno (University of South Carolina) and Elias Bou-Harb (University of Texas at San Antonio)

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Detecting Obfuscated Function Clones in Binaries using Machine Learning

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

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