3 research outputs found
Securing Signal-free Intersections against Strategic Jamming Attacks: A Macroscopic Approach
We consider the security-by-design of a signal-free intersection for
connected and autonomous vehicles in the face of strategic jamming attacks. We
use a fluid model to characterize macroscopic traffic flow through the
intersection, where the saturation rate is derived from a vehicle coordination
algorithm. We model jamming attacks as sudden increase in communication latency
induced on vehicle-to-infrastructure connectivity; such latency triggers the
safety mode for vehicle coordination and thus reduces the intersection
saturation rate. A strategic attacker selects the attacking rate, while a
system operator selects key design parameters, either the saturation rate or
the recovery rate. Both players' actions induce technological costs and jointly
determine the mean travel delay. By analyzing the equilibrium of the security
game, we study the preferable level of investment in the intersection's nominal
discharging capability or recovery capability, for balance between
hardware/infrastructure cost and security-by-design.Comment: Submitted to 61st IEEE Conference on Decision and Contro
A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks
Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) 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 future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions