In-Vehicle Digital Forensics for Connected and Automated Vehicles With Public Auditing

• Published in: IEEE internet of things journal Vol. 11; no. 4; pp. 6368 - 6383
• Main Authors: Li, Jiangtao, Song, Zhaoheng, Zhang, Zihou, Li, Yufeng, Cao, Chenhong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accidents; Algorithms; Autonomous vehicles; Cloud computing; Computer forensics; Computing costs; Data mining; Digital forensics; Forensic computing; Forensic sciences; In vehicle; in-vehicle data; Integrity; Internet of Things; Power consumption; public auditing; Security; Telematics; Vehicles
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3310578

Connected and autonomous vehicles produce a substantial amount of data that is essential for implementing advanced and intelligent features. Given the importance and the volume of in-vehicle data, storing it in the cloud for later extraction as critical evidence for vehicle digital forensics is a logical choice. However, ensuring the security of forensic data against tampering and forgery attacks throughout the process is a significant challenge. Existing solutions typically assume that vehicles will generate and upload the in-vehicle data to the cloud honestly. In reality, it may be necessary to prove whether the vehicle has uploaded authentic driving-related data in case of disputes about data authenticity. To address this issue, we propose an in-vehicle digital forensic scheme with public auditing, enabling anyone to perform a public auditing algorithm to check whether the data has been modified. The proposal is based on a process-oriented data integrity proof method that enables a vehicle to generate public verifiable integrity proof. Furthermore, we evaluated the practicality of our scheme by assessing its computational and communication overhead. In terms of computational cost, our proposed scheme demonstrates a power consumption of 0.0385 kWh per 100 km at a speed of 60 km/h. Regarding communication delay, our method exhibits a 50.1% decrease compared to similar approaches.