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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. 

To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem — the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize fault-tolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms. In this work, we propose TriSAS—a novel inter-SAS coordination mechanism to facilitate secure, efficient, and dependable spectrum allocation that is fully compatible with the existing SAS infrastructure. TriSAS decomposes the coordination process into two phases including input synchronization and decision finalization. The firstphase ensures participants share a common input set while the second one fulfills a fair and verifiable spectrum allocation selec- tion, which is generated efficiently via SAS proposers’ proprietary allocation algorithms and evaluated by a customized designed allocation evaluation algorithm (AEA), in the face of no more than one-third of malicious participants. We implemented a prototype of TriSAS on the AWS cloud computing platform and evaluated its throughput and latency performance. The results show that TriSAS achieves high transaction throughput and low latency under various practical settings. 

Dynamic spectrum sharing has emerged as a promising solution to address the spectrum scarcity challenge. Currently, the FCC has designated several Spectrum Access Systems (SAS) administrators to deploy their SAS that coordinates the usage of the certificated shared band(s) such as the 3.55-3.7 GHz CBRS band. The SAS ensures that the incumbent’s access to the shared band is guaranteed while also granting commercial users access rights when the incumbents are not present. However, explicitly sharing the spectrum band(s) information among participants raises privacy concerns. Certain participants, such as curious SAS administrators, have the ability to deduce the confidential operational patterns of the incumbents through the Environmental Sensing Capability (ESC) or Incumbent Informing Capability (IIC) notifications. Additionally, a curious SAS administrator may obtain the client’s operational information of other SAS administrators throughout the process of inter-SAS coordination. We propose Pri-Share, a novel privacy-preserving spectrum sharing paradigm that tailors the threshold-based private set union (PSU) and homomorphic encryption (HE) techniques to address the aforementioned privacy problems. Specifically, it enables all parties to jointly compute a unified spectrum allocation plan to resolve the potential conflicts between different parties while safeguarding the confidentiality of each stakeholder’s spectrum requirements and usage. Pri-Share also ensures that while a curious participant might ascertain the usage of a particular spectrum band, they are unable to deduce the precise identity of the party utilizing it. Besides, Pri-Share adheres to the key spectrum allocation regulations outlined by FCC (part 96), such as assurance of access rights for various priority levels. Our implementation result shows that Pri-Share can be achieved with notable computational and communication efficiency,