Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems
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Abstract (arXiv)
Cooperative Intelligent Transportation Systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and infrastructure, thereby creating a highly dynamic and heterogeneously managed ad-hoc network. It is these network properties that make it a challenging task to protect integrity of the data and guarantee its correctness. A major component is the problem that traditional security mechanisms like PKI-based asymmetric cryptography only exclude outsider attackers that do not possess key material. However, because attackers can be insiders within the network (i.e., possess valid key material), this approach cannot detect all possible attacks. In this survey, we present misbehavior detection mechanisms that can detect such insider attacks based on attacker behavior and information analysis. In contrast to well-known intrusion detection for classical IT systems, these misbehavior detection mechanisms analyze information semantics to detect attacks, which aligns better with highly application-tailored communication protocols foreseen for cITS. In our survey, we provide an extensive introduction to the cITS ecosystem and discuss shortcomings of PKI-based security. We derive and discuss a classification for misbehavior detection mechanisms, provide an in-depth overview of seminal papers on the topic, and highlight open issues and possible future research trends.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 3.
6 papers.
- 2022A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networkscited by 11
- 2024Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Modelscited by 2
- 2025Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defensescited by 1
- 2025Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detectioncited by 1
- 2021Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehiclescited by 0
- 2025Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Surveycited by 0
- 2025Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection17 Dec 2025
- 2025Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey10 May 2025
- 2025Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses6 Feb 2025
- 2024Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models16 May 2024
- 2022A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks25 Jan 2022
- 2021Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles2 Aug 2021