Mahdi Akil (Karlstad University), Leonardo Martucci (Karlstad University), Jaap-Henk Hoepman (Radboud University)

In vehicular ad hoc networks (VANETs), vehicles exchange messages to improve traffic and passengers’ safety. In VANETs, (passive) adversaries can track vehicles (and their drivers) by analyzing the data exchanged in the network. The use of privacy-enhancing technologies can prevent vehicle tracking but solutions so far proposed either require an intermittent connection to a fixed infrastructure or allow vehicles to generate concurrent pseudonyms which could lead to identity-based (Sybil) attacks. In this paper, we propose an anonymous authentication scheme that does not require a connection to a fixed infrastructure during operation and is not vulnerable to Sybil attacks. Our scheme is built on attribute-based credentials and short lived pseudonyms. In it, vehicles interact with a central authority only once, for registering themselves, and then generate their own pseudonyms without interacting with other devices, or relying on a central authority or a trusted third party. The pseudonyms are periodically refreshed, following system wide epochs.

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Analysing Adversarial Threats to Rule-Based Local-Planning Algorithms for Autonomous...

Andrew Roberts (Tallinn University of Technology), Mohsen Malayjerdi (Tallinn University of Technology), Mauro Bellone (Tallinn University of Technology), Olaf Maennel (The University of Adelaide), Ehsan Malayjerdi (Tallinn University of Technology)

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Detection and Resolution of Control Decision Anomalies

Prof. Kang Shin (Kevin and Nancy O'Connor Professor of Computer Science, and the Founding Director of the Real-Time Computing Laboratory (RTCL) in the Electrical Engineering and Computer Science Department at the University of Michigan)

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Secure Control of Connected and Automated Vehicles Using Trust-Aware...

H M Sabbir Ahmad, Ehsan Sabouni, Akua Dickson (Boston University), Wei Xiao (Massachusetts Institute of Technology), Christos Cassandras, Wenchao Li (Boston University)

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CANtropy: Time Series Feature Extraction-Based Intrusion Detection Systems for...

Md Hasan Shahriar, Wenjing Lou, Y. Thomas Hou (Virginia Polytechnic Institute and State University)

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