Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), J. Scot Ransbottom (Virginia Tech)

Attackers have found numerous vulnerabilities in the Electronic Control Units (ECUs) of modern vehicles, enabling them to stop the car, control its brakes, and take other potentially disruptive actions. Many of these attacks were possible because the vehicles had insecure In-Vehicle Networks (IVNs), where ECUs could send any message to each other. For example, an attacker who compromised an infotainment ECU might be able to send a braking message to a wheel. In this work, we introduce a scheme based on distributed firewalls to block these unauthorized messages according to a set “security policy” defining what transmissions each ECU should be able to send and receive. We leverage the topology of new switched, zonal networks to authenticate messages without cryptography, using Ternary Content Addressable Memory (TCAMs) to enforce the policy at wire-speed. Crucially, our approach minimizes the security burden on edge ECUs and places control in a set of hardened zonal gateways. Through an OMNeT++ simulation of a zonal IVN, we demonstrate that our scheme has much lower overhead than modern cryptography-based approaches and allows for realtime, low-latency (​<0.1 ms) traffic.

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TrustSketch: Trustworthy Sketch-based Telemetry on Cloud Hosts

Zhuo Cheng (Carnegie Mellon University), Maria Apostolaki (Princeton University), Zaoxing Liu (University of Maryland), Vyas Sekar (Carnegie Mellon University)

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LiDAR Spoofing Meets the New-Gen: Capability Improvements, Broken Assumptions,...

Takami Sato (University of California, Irvine), Yuki Hayakawa (Keio University), Ryo Suzuki (Keio University), Yohsuke Shiiki (Keio University), Kentaro Yoshioka (Keio University), Qi Alfred Chen (University of California, Irvine)

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SENSE: Enhancing Microarchitectural Awareness for TEEs via Subscription-Based Notification

Fan Sang (Georgia Institute of Technology), Jaehyuk Lee (Georgia Institute of Technology), Xiaokuan Zhang (George Mason University), Meng Xu (University of Waterloo), Scott Constable (Intel), Yuan Xiao (Intel), Michael Steiner (Intel), Mona Vij (Intel), Taesoo Kim (Georgia Institute of Technology)

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Attributions for ML-based ICS Anomaly Detection: From Theory to...

Clement Fung (Carnegie Mellon University), Eric Zeng (Carnegie Mellon University), Lujo Bauer (Carnegie Mellon University)

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