# A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

arXiv: https://arxiv.org/abs/2201.10500 · HTML: https://mlsanity.com/paper/2201.10500/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 222 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 21 of 21)

- [MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2105.13289/) (2021)
- [Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward](https://mlsanity.com/paper/1905.12762/) (2019)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs](https://mlsanity.com/paper/1804.06701/) (2018)
- [AI based Service Management for 6G Green Communications](https://mlsanity.com/paper/2101.01588/) (2021)
- [Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense](https://mlsanity.com/paper/1909.08092/) (2019)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/1610.06810/) (2016)
- [Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model](https://mlsanity.com/paper/1805.06104/) (2018)
- [Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach](https://mlsanity.com/paper/2111.12360/) (2021)
- [Crossfire Attack Detection using Deep Learning in Software Defined ITS Networks](https://mlsanity.com/paper/1812.03639/) (2018)
- [Differentially Private Collaborative Intrusion Detection Systems For VANETs](https://mlsanity.com/paper/2005.00703/) (2020)
- [Resilient Cooperative Adaptive Cruise Control for Autonomous Vehicles Using Machine Learning](https://mlsanity.com/paper/2103.10533/) (2021)
- [Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles](https://mlsanity.com/paper/2108.00833/) (2021)
- [Misbehavior Detection Using Collective Perception under Privacy Considerations](https://mlsanity.com/paper/2111.03461/) (2021)
- [5G Software Defined Vehicular Networks](https://mlsanity.com/paper/1702.03675/) (2017)
- [A Plausibility-based Fault Detection Method for High-level Fusion Perception Systems](https://mlsanity.com/paper/2009.14756/) (2020)
- [V2X Misbehavior and Collective Perception Service: Considerations for Standardization](https://mlsanity.com/paper/2112.02184/) (2021)
- [A survey on pseudonym changing strategies for Vehicular Ad-Hoc Networks](https://mlsanity.com/paper/1704.00679/) (2017)
- [Security of 5G-V2X: Technologies, Standardization and Research Directions](https://mlsanity.com/paper/1905.09555/) (2019)
- [Co-channel Coexistence: Let ITS-G5 and Sidelink C-V2X Make Peace](https://mlsanity.com/paper/2003.09510/) (2020)

## Built on by (most cited first, 11 of 11)

- [Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV](https://mlsanity.com/paper/2407.02969/) (2024)
- [A Superalignment Framework in Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2406.05651/) (2024)
- [Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks](https://mlsanity.com/paper/2407.03070/) (2024)
- [GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching](https://mlsanity.com/paper/2408.10286/) (2024)
- [MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection](https://mlsanity.com/paper/2407.18462/) (2024)
- [Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models](https://mlsanity.com/paper/2405.09903/) (2024)
- [AI for Agile development: a Meta-Analysis](https://mlsanity.com/paper/2305.08093/) (2023)
- [Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection](https://mlsanity.com/paper/2512.15503/) (2025)
- [MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network](https://mlsanity.com/paper/2403.04293/) (2024)
- [Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey](https://mlsanity.com/paper/2505.07882/) (2025)
- [PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks](https://mlsanity.com/paper/2605.06833/) (2026)

## Abstract (arXiv)

Significant progress has been made towards deploying Vehicle-to-Everything (V2X) technology. Integrating V2X with 5G has enabled ultra-low latency and high-reliability V2X communications. However, while communication performance has enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure our future roads. Many V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. Yet, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper present a comprehensive survey and classification of ML-based MDSs. We analyze and discuss them from both security and ML perspectives. Then, we give some learned lessons and recommendations helping in developing, validating, and deploying ML-based MDSs. Finally, we highlight open research and standardization issues with some future directions.