Mohit Kumar Jangid (Ohio State University) and Zhiqiang Lin (Ohio State University)

Being safer, cleaner, and more efficient, connected and autonomous vehicles (CAVs) are expected to be the dominant vehicles of future transportation systems. However, there are enormous security and privacy challenges while also considering the efficiency and and scalability. One key challenge is how to efficiently authenticate a vehicle in the ad-hoc CAV network and ensure its tamper-resistance, accountability, and non-repudiation. In this paper, we present the design and implementation of Vehicle-to-Vehicle (V2V) protocol by leveraging trusted execution environment (TEE), and show how this TEE-based protocol achieves the objective of authentication, privacy, accountability and revocation as well as the scalability and efficiency. We hope t hat our TEE-based V2V protocol can inspire further research into CAV security and privacy, particularly how to leverage TEE to solve some of the hard problems and make CAV closer to practice.

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Interpretable Federated Transformer Log Learning for Cloud Threat Forensics

Gonzalo De La Torre Parra (University of the Incarnate Word, TX, USA), Luis Selvera (Secure AI and Autonomy Lab, The University of Texas at San Antonio, TX, USA), Joseph Khoury (The Cyber Center For Security and Analytics, University of Texas at San Antonio, TX, USA), Hector Irizarry (Raytheon, USA), Elias Bou-Harb (The Cyber Center For…

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Demo #8: Identifying Drones Based on Visual Tokens

Ben Nassi (Ben-Gurion University of the Negev), Elad Feldman (Ben-Gurion University of the Negev), Aviel Levy (Ben-Gurion University of the Negev), Yaron Pirutin (Ben-Gurion University of the Negev), Asaf Shabtai (Ben-Gurion University of the Negev), Ryusuke Masuoka (Fujitsu System Integration Laboratories) and Yuval Elovici (Ben-Gurion University of the Negev)

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Evaluating Susceptibility of VPN Implementations to DoS Attacks Using...

Fabio Streun (ETH Zurich), Joel Wanner (ETH Zurich), Adrian Perrig (ETH Zurich)

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Demo #2: Sequential Attacks on Kalman Filter-Based Forward Collision...

Yuzhe Ma, Jon Sharp, Ruizhe Wang, Earlence Fernandes, and Jerry Zhu (University of Wisconsin–Madison)

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