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Emergent vehicles will support a variety of connected applications, where a vehicle communicates with other vehicles or with the infrastructure to make a variety of decisions. Cooperative connected applications provide a critical foundational pillar for autonomous driving, and hold the promise of improving road safety, efficiency and environmental sustainability. However, they also induce a large and easily exploitable attack surface: an adversary can manipulate vehicular communications to subvert functionality of participating individual vehicles, cause catastrophic accidents, or bring down the transportation infrastructure. In this paper we outline a potential direction to address this critical problem through a resiliency framework, REDEM, based on machine learning. REDEM has several interesting features, including (1) smooth integration with the architecture of the underlying application, (2) ability to handle diverse communication attacks within the same underlying foundation, and (3) real-time detection and mitigation capability. We present the vision of REDEM, identify some key challenges to be addressed in its realization, and discuss the kind of evaluation/analysis necessary for its viability. We also present initial results from one instantiation of REDEM introducing resiliency in Cooperative Adaptive Cruise Control (CACC).
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This content will become publicly available on May 25, 2026
A Comprehensive Survey on Basic Safety Message Attacks and Their Detection
Vehicular Ad-hoc Networks (VANETs) are a crucial component of Cooperative Intelligent Transportation Systems (C-ITS), enabling vehicles to communicate and share vital information to enhance road safety and efficiency. Basic Safety Messages (BSMs), periodically broadcast by vehicles to provide real-time kinematic data, form the foundation of numerous safety applications within VANETs. Ensuring the security of BSMs is paramount, as malicious entities can exploit vulnerabilities to launch attacks that could have catastrophic consequences. In this study, we provide a comprehensive analysis of BSM attacks and detection mechanisms in VANETs. We begin by outlining the system model, security requirements, and attacker models relevant to BSMs. Then, we categorize and describe a range of attacks, from simple position falsification to more sophisticated and evasive techniques, such as the SixPack attack. We also classify existing attack detection methods into machine learning-based, deep learning-based, plausibility and consistency-based, and software-defined networking (SDN)-based mechanisms, analyzing their effectiveness and limitations. Additionally, we highlight the challenges in securing BSMs, such as the trade-off between model accuracy and real-time performance. Future research directions are also discussed. This survey paper serves as a foundational step towards building safe, secure, and reliable cooperative intelligent transportation systems and their associated applications.
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- PAR ID:
- 10627080
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- ISBN:
- 978-3-031-89359-9
- Page Range / eLocation ID:
- 110 to 130
- Subject(s) / Keyword(s):
- VANET Security Basic Safety Message BSM Attack Misbehavior Detection Position Falsification Plausibility Check
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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