Biblio

The Internet of Vehicles (IoV) has a tremendous prospect for numerous vehicular applications. IoV enables vehicles to transmit data to improve roadway safety and efficiency. Data security is essential for increasing the security and privacy of vehicle and roadway infrastructures in IoV systems. Several researchers proposed numerous solutions to address security and privacy issues in IoV systems. However, these issues are not proper solutions that lack data authentication and verification protocols. In this paper, a blockchain-enabled automated data management system for vehicles has been proposed and demonstrated. This work enables automated data verification and authentication using smart contracts. Certified organizations can only access vehicle data uploaded by the vehicle user to the Interplanetary File System (IPFS) server through that vehicle user’s consent. The proposed system increases the security of vehicles and data. Vehicle privacy is also maintained here by increasing data privacy.

Blockchain is developing rapidly in various domains for its security. Nowadays, one of the most crucial fundamental concerns is internet security. Blockchain is a novel solution to enhance the security of network applications. However, there are no precise frameworks to secure the Internet of Vehicle (IoV) using Blockchain technology. In this paper, a blockchain-based smart internet of vehicle (BSIoV) framework has been proposed due to the cooperative, collaborative, transparent, and secure characteristics of Blockchain. The main contribution of the proposed work is to connect vehicle-related authorities together to fix a secure and transparent vehicle-to-everything (V2X) communication through the peer-to-peer network connection and provide secure services to the intelligent transport systems. A key management strategy has been included to identify a vehicle in this proposed system. The proposed framework can also provide a significant solution for the data security and safety of the connected vehicles in blockchain network.

High-end vehicles incorporate about one hundred computers; physical and virtualized ones; self-driving vehicles even more. This allows a plethora of attack combinations. This paper demonstrates how to assess exploitability risks of vehicular on-board networks via automatically generated and analyzed attack graphs. Our stochastic model and algorithm combine all possible attack vectors and consider attacker resources more efficiently than Bayesian networks. We designed and implemented an algorithm that assesses a compilation of real vehicle development documents within only two CPU minutes, using an average of about 100 MB RAM. Our proof of concept "Security Analyzer for Exploitability Risks" (SAlfER) is 200 to 5 000 times faster and 40 to 200 times more memory-efficient than an implementation with UnBBayes1. Our approach aids vehicle development by automatically re-checking the architecture for attack combinations that may have been enabled by mistake and which are not trivial to spot by the human developer. Our approach is intended for and relevant for industrial application. Our research is part of a collaboration with a globally operating automotive manufacturer and is aimed at supporting the security of autonomous, connected, electrified, and shared vehicles.

Semi-autonomous driver assists are already widely deployed and fully autonomous cars are progressively leaving the realm of laboratories. This evolution coexists with a progressive connectivity and cooperation, creating important safety and security challenges, the latter ranging from casual hackers to highly-skilled attackers, requiring a holistic analysis, under the perspective of fully-fledged ecosystems of autonomous and cooperative vehicles. This position paper attempts at contributing to a better understanding of the global threat plane and the specific threat vectors designers should be attentive to. We survey paradigms and mechanisms that may be used to overcome or at least mitigate the potential risks that may arise through the several threat vectors analyzed.

With the application and promotion of electric vehicles, vehicle security problems caused by actuator reliability have become increasingly prominent. Firstly, the paper analyses and sums motor failure modes and their effects of permanent magnet synchronous motor (PMSM) , which is commonly used on electric vehicles. And then design a hierarchical structure of the vehicle control strategies and the corresponding algorithms, and adjust based on the different failure modes. Finally conduct simulation conditions in CarSim environment. Verify the control strategy and algorithm can maintain vehicle stability and reduce the burden on driver under motor failure conditions.