Token-based Vehicular Security System (TVSS): Scalable, Secure, Low-latency Public Key Infrastructure for Connected Vehicles
- URL: http://arxiv.org/abs/2402.18365v1
- Date: Wed, 28 Feb 2024 14:35:52 GMT
- Title: Token-based Vehicular Security System (TVSS): Scalable, Secure, Low-latency Public Key Infrastructure for Connected Vehicles
- Authors: Abdulrahman Bin Rabiah, Anas Alsoliman, Yugarshi Shashwat, Silas Richelson, Nael Abu-Ghazaleh,
- Abstract summary: We present TVSS, a new VPKI system which improves drastically over prior work in the area.
TVSS leverages the idea of unforgeable tokens to enable rapid verification at the road side units (RSUs), which are part of the road infrastructure at the edge of the network.
Notably, we are able to execute the bottleneck operation of our scheme with a stationary RSU while traveling at highway speeds.
- Score: 1.062554955743753
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Connected and Autonomous vehicles stand to drastically improve the safety and efficiency of the transportation system in the near future while also reducing pollution. These systems leverage communication to coordinate among vehicles and infrastructure in service of a number of safety and efficiency driver assist and even fully autonomous applications. Attackers can compromise these systems in a number of ways including by falsifying communication messages, making it critical to support security mechanisms that can operate and scale in dynamic scenarios. Towards this end, we present TVSS, a new VPKI system which improves drastically over prior work in the area (including over SCMS; the US department of transportation standard for VPKI). TVSS leverages the idea of unforgeable tokens to enable rapid verification at the road side units (RSUs), which are part of the road infrastructure at the edge of the network. This edge based solution enables agile authentication by avoiding the need for back-end servers during the potentially short contact time between a moving vehicle and the infrastructure. It also results in several security advantages: (1) Scalable Revocation: it greatly simplifies the revocation problem, a difficult problem in large scale certificate systems; and (2) Faster Refresh: Vehicles interact more frequently with the system to refresh their credentials, improving the privacy of the system. We provide a construction of the system and formally prove its security. Field experiments on a test-bed we develop consisting of on-board units (OBUs) and RSUs shows substantial reduction in the latency of refreshing credentials compared to SCMS, allowing the system to work even with smaller window of connectivity when vehicles are moving at higher speeds. Notably, we are able to execute the bottleneck operation of our scheme with a stationary RSU while traveling at highway speeds .
Related papers
- Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads [11.623582669220115]
This paper introduces a novel Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System (VVCCS)
VVCCS revolutionizes macro-scope traffic planning and collision avoidance in autonomous driving.
We also developed a comprehensive multi-modal perception system with multi-objective tracking and radar sensing.
arXiv Detail & Related papers (2024-10-23T05:59:55Z) - Unique ID based Trust Scheme for Improved IoV Wireless Sensor Network Security Against Power Controlled Sybil Attacks [1.906179410714637]
Wireless sensor networks (WSNs) are widely used in vehicular networks to support Vehicle-to-Everything (V2X) communications.
WSNs face security challenges due to their distributed nature and resource limited modules.
This paper proposes a unique identification based trust path routing scheme (UITrust) to avoid Sybil attacks.
arXiv Detail & Related papers (2024-10-05T07:20:55Z) - Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks.
In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
arXiv Detail & Related papers (2024-03-29T12:01:31Z) - Cyber-Twin: Digital Twin-boosted Autonomous Attack Detection for Vehicular Ad-Hoc Networks [8.07947129445779]
The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS)
VANETs are increasingly susceptible to cyberattacks, such as jamming and distributed denial of service (DDoS) attacks.
Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity.
This study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs.
arXiv Detail & Related papers (2024-01-25T08:05:41Z) - MSight: An Edge-Cloud Infrastructure-based Perception System for
Connected Automated Vehicles [58.461077944514564]
This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles.
MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction.
Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
arXiv Detail & Related papers (2023-10-08T21:32:30Z) - When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures.
We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN.
Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
arXiv Detail & Related papers (2023-06-09T14:33:26Z) - Reinforcement Learning based Cyberattack Model for Adaptive Traffic
Signal Controller in Connected Transportation Systems [61.39400591328625]
In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time.
This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes.
One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network.
An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
arXiv Detail & Related papers (2022-10-31T20:12:17Z) - Synergistic Redundancy: Towards Verifiable Safety for Autonomous
Vehicles [10.277825331268179]
We propose Synergistic Redundancy (SR) a safety architecture for complex cyber physical systems, like Autonomous Vehicle (AV)
SR provides verifiable safety guarantees against specific faults by decoupling the mission and safety tasks of the system.
Close coordination with the mission layer allows easier and early detection of safety critical faults in the system.
arXiv Detail & Related papers (2022-09-04T23:52:03Z) - Differentiable Control Barrier Functions for Vision-based End-to-End
Autonomous Driving [100.57791628642624]
We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving.
We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
arXiv Detail & Related papers (2022-03-04T16:14:33Z) - BarrierNet: A Safety-Guaranteed Layer for Neural Networks [50.86816322277293]
BarrierNet allows the safety constraints of a neural controller be adaptable to changing environments.
We evaluate them on a series of control problems such as traffic merging and robot navigations in 2D and 3D space.
arXiv Detail & Related papers (2021-11-22T15:38:11Z) - Trust-aware Control for Intelligent Transportation Systems [0.20415910628419062]
We propose a framework for using the quantified trustworthiness of agents to enable trust-aware coordination and control.
We show how to synthesize trust-aware controllers using an approach based on reinforcement learning.
We develop a trust-aware version called AIM-Trust that leads to lower accident rates in scenarios consisting of a mixture of trusted and untrusted agents.
arXiv Detail & Related papers (2021-11-08T03:02:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.