An Intelligent Automated System for Detecting Malicious Vehicles in Intelligent Transportation Systems

【Author】 Ashfaq, Tehreem; Khalid, Rabiya; Yahaya, Adamu Sani; Aslam, Sheraz; Azar, Ahmad Taher; Alkhalifah, Tamim; Tounsi, Mohamed

【Source】SENSORS

【影响因子】3.847

【Abstract】The exponential growth of intelligent vehicles(IVs) development has resulted in a complex network. As the number of IVs in a network increases, so does the number of connections. As a result, a great deal of data is generated. This complexity leads to insecure communication, traffic congestion, security, and privacy issues in vehicular networks (VNs). In addition, detecting malicious IVs, data integration, and data validation are major issues in VNs that affect network performance. A blockchain-based model for secure communication and malicious IV detection is proposed to address the above issues. In addition, this system also addresses data integration and transaction validation using an encryption scheme for secure communication. A multi-chain concept separates the legitimate and malicious data into two chains: the Integrity chain (I-chain) and Fraud chain (F-chain). This multi-chain mechanism solves the storage problem and reduces the computing power. The integration of blockchain in the proposed model provides privacy, network security, transparency, and immutability. To address the storage issue, the InterPlanetary File System (IPFS) is integrated with Certificate Authority (CA). A reputation mechanism is introduced to detect malicious IVs in the network based on ratings. This reputation mechanism is also used to prevent Sybil attack. The evaluation of the proposed work is based on the cost of smart contracts and computation time. Furthermore, two attacker models are presented to prevent the selfish mining attack and the Sybil attack. Finally, a security analysis of the proposed smart contracts with their security vulnerabilities is also presented.

【Keywords】certificate authority; intelligent vehicles; InterPlanetary File System; vehicular network

【发表时间】2022 SEP

【收录时间】2022-09-15

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-交通领域