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1. Privacy Preserving Misbehavior Detection in IoV Using Federated Machine Learning

   https://doi.org/10.1109/CCNC49032.2021.9369513  

   Uprety, Aashma; Rawat, Danda B.; Li, Jiang (January 2021, 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC))

   null (Ed.)

   Data falsification attack in Vehicular Ad hoc Networks (VANET) for the Internet of Vehicles (IoV) is achieved by corrupting the data exchanged between nodes with false information. Data is the most valuable asset these days from which many analyses and results can be drawn out. But the privacy concern raised by users has become the greatest hindrance in performing data analysis. In IoV, misbehavior detection can be performed by creating a machine learning model from basic safety message (BSM) dataset of vehicles. We propose a privacy-preserving misbehavior detecting system for IoV using Federated Machine Learning. Vehicles in VANET for IoV are given the initial dull model to locally train using their own local data. On doing this we get a collective smart model that can classify Position Falsification attack in VANET using the data generated by each vehicle. All this is done without actually sharing the data with any third party to perform analysis. In this paper, we compare the performance of the attack detection model trained by using a federated and central approach. This training method trains the model on a different kind of position falsification attack by using local BSM data generated on each vehicle. 

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2. A Comprehensive Survey on Basic Safety Message Attacks and Their Detection

   https://doi.org/10.1007/978-3-031-89360-5_7  

   Mohamed, Ahmed; Xie, Mengjun (May 2025, Springer Nature Switzerland)

   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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3. REPLACE: Real-time Security Assurance in Vehicular Platoons Against V2V Attacks

   Boddupalli, S; Hegde, A; Ray, S. (January 2021, 24th IEEE International Conference on Intelligent Transportation (ITSC 2021))

   null (Ed.)

   Connected Autonomous Vehicular (CAV) platoon refers to a group of vehicles that coordinate their movements and operate as a single unit. The vehicle at the head acts as the leader of the platoon and determines the course of the vehicles following it. The follower vehicles utilize Vehicle-to-Vehicle (V2V) communication and automated driving support systems to automatically maintain a small fixed distance between each other. Reliance on V2V communication exposes platoons to several possible malicious attacks which can compromise the safety, stability, and efficiency of the vehicles. We present a novel distributed resiliency architecture, RePLACe for CAV platoon vehicles to defend against adversaries corrupting V2V communication reporting preceding vehicle position. RePLACe is unique in that it can provide real-time defense against a spectrum of communication attacks. RePLACe provides systematic augmentation of a platoon controller architecture with real-time detection and mitigation functionality using machine learning. Unlike computationally intensive cryptographic solutions RePLACe accounts for the limited computation capabilities provided by automotive platforms as well as the real-time requirements of the application. Furthermore, unlike control-theoretic approaches, the same framework works against the broad spectrum of attacks. We also develop a systematic approach for evaluation of resiliency of CAV applications against V2V attacks. We perform extensive experimental evaluation to demonstrate the efficacy of RePLACe. 

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4. An Interaction Provenance-based Trust Management Scheme For Connected Vehicles

   https://doi.org/10.1109/CCNC49033.2022.9700580  

   Hoque, Mohammad Aminul; Hasan, Ragib (January 2022, IEEE 19th Annual Consumer Communications & Networking Conference (CCNC))

   Connected vehicles (CVs) have facilitated the development of intelligent transportation system that supports critical safety information sharing with minimum latency. However, CVs are vulnerable to different external and internal attacks. Though cryptographic techniques can mitigate external attacks, preventing internal attacks imposes challenges due to authorized but malicious entities. Thwarting internal attacks require identifying the trustworthiness of the participating vehicles. This paper proposes a trust management framework for CVs using interaction provenance that ensures privacy, considers both in-vehicle and vehicular network security incidents, and supports flexible security policies. For this purpose, we present an interaction provenance recording and trust management protocol. Different events are extracted from interaction provenance, and trustworthiness is calculated using fuzzy policies based on the events. 

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5. VehiGAN : Generative Adversarial Networks for Adversarially Robust V2X Misbehavior Detection Systems

   https://doi.org/10.1145/3745787  

   Shahriar, Md Hasan; Ansari, Mohammad Raashid; Monteuuis, Jean-Philippe; Haque, Md Shahedul; Chen, Cong; Petit, Jonathan; Hou, Y Thomas; Lou, Wenjing (July 2025, ACM Transactions on Cyber-Physical Systems)

   Vehicle-to-Everything (V2X) communication enables vehicles to communicate with other vehicles and roadside infrastructure, enhancing traffic management and improving road safety. However, the open and decentralized nature of V2X networks exposes them to various security threats, especially misbehaviors, necessitating a robust Misbehavior Detection System (MBDS). While Machine Learning (ML) has proved effective in different anomaly detection applications, the existing ML-based MBDSs have shown limitations in generalizing due to the dynamic nature of V2X and insufficient and imbalanced training data. Moreover, they are known to be vulnerable to adversarial ML attacks. On the other hand, Generative Adversarial Networks (GAN) possess the potential to mitigate the aforementioned issues and improve detection performance by synthesizing unseen samples of minority classes and utilizing them during their model training. Therefore, we propose the first application of GAN to design an MBDS that detects any misbehavior and ensures robustness against adversarial perturbation. In this article, we present several key contributions. First, we propose an advanced threat model for stealthy V2X misbehavior where the attacker can transmit malicious data and mask it using adversarial attacks to avoid detection by ML-based MBDS. We formulate two categories of adversarial attacks against the anomaly-based MBDS. Later, in the pursuit of a generalized and robust GAN-based MBDS, we train and evaluate a diverse set of Wasserstein GAN (WGAN) models and presentVehicularGAN(VehiGAN), an ensemble of multiple top-performing WGANs, which transcends the limitations of individual models and improves detection performance. We present a physics-guided data preprocessing technique that generates effective features for ML-based MBDS. In the evaluation, we leverage the state-of-the-art V2X attack simulation tool VASP to create a comprehensive dataset of V2X messages with diverse misbehaviors. Evaluation results show that in 20 out of 35 misbehaviors,VehiGANoutperforms the baseline and exhibits comparable detection performance in other scenarios. Particularly,VehiGANexcels in detecting advanced misbehaviors that manipulate multiple fields in V2X messages simultaneously, replicating unique maneuvers. Moreover,VehiGANprovides approximately 92% improvement in false positive rate under powerful adaptive adversarial attacks, and possesses intrinsic robustness against other adversarial attacks that target the false negative rate. Finally, we make the data and code available for reproducibility and future benchmarking, available athttps://github.com/shahriar0651/VehiGAN. 

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