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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REPLICAWATCHER: Training-less Anomaly Detection in Containerized Microservices

Asbat El Khairi (University of Twente), Marco Caselli (Siemens AG), Andreas Peter (University of Oldenburg), Andrea Continella (University of Twente)

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Pencil: Private and Extensible Collaborative Learning without the Non-Colluding...

Xuanqi Liu (Tsinghua University), Zhuotao Liu (Tsinghua University), Qi Li (Tsinghua University), Ke Xu (Tsinghua University), Mingwei Xu (Tsinghua University)

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Front-running Attack in Sharded Blockchains and Fair Cross-shard Consensus

Jianting Zhang (Purdue University), Wuhui Chen (Sun Yat-sen University), Sifu Luo (Sun Yat-sen University), Tiantian Gong (Purdue University), Zicong Hong (The Hong Kong Polytechnic University), Aniket Kate (Purdue University)

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Timing Channels in Adaptive Neural Networks

Ayomide Akinsanya (Stevens Institute of Technology), Tegan Brennan (Stevens Institute of Technology)

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