# Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2105.15035 · HTML: https://mlsanity.com/paper/2105.15035/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 226 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 30 of 47)

- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Weight Poisoning Attacks on Pre-trained Models](https://mlsanity.com/paper/2004.06660/) (2020)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [EIE: Efficient Inference Engine on Compressed Deep Neural Network](https://mlsanity.com/paper/1602.01528/) (2016)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles](https://mlsanity.com/paper/2001.11231/) (2020)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [Is feature selection secure against training data poisoning?](https://mlsanity.com/paper/1804.07933/) (2018)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car](https://mlsanity.com/paper/1704.07911/) (2017)
- [Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models](https://mlsanity.com/paper/2001.03274/) (2020)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems](https://mlsanity.com/paper/2003.07859/) (2020)
- [Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward](https://mlsanity.com/paper/1905.12762/) (2019)
- [Characterizing Attacks on Deep Reinforcement Learning](https://mlsanity.com/paper/1907.09470/) (2019)
- [Federated Learning for Ultra-Reliable Low-Latency V2V Communications](https://mlsanity.com/paper/1805.09253/) (2018)
- [Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues](https://mlsanity.com/paper/1809.08707/) (2018)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [Tree-based Intelligent Intrusion Detection System in Internet of Vehicles](https://mlsanity.com/paper/1910.08635/) (2019)
- [Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems](https://mlsanity.com/paper/1805.00983/) (2018)
- [VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs](https://mlsanity.com/paper/1804.06701/) (2018)

## Built on by (most cited first, 7 of 7)

- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI](https://mlsanity.com/paper/2410.09043/) (2024)
- [Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles](https://mlsanity.com/paper/2108.00833/) (2021)
- [GFCL: A GRU-based Federated Continual Learning Framework against Data Poisoning Attacks in IoV](https://mlsanity.com/paper/2204.11010/) (2022)
- [F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks](https://mlsanity.com/paper/2607.04698/) (2026)

## Abstract (arXiv)

Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.