T-Box: A Forensics-Enabled Trusted Automotive Data Recording Method

Seungho Lee, Wonsuk Choi, Hyo Jin Jo, Dong Hoon Lee

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Modern vehicles are equipped with numerous electric control units which exchange vehicular status data, providing drivers with convenience, efficiency, and safety. In addition, the autonomous vehicles adopt various sensors that produce high volumes of high-speed data to process and assess internal and external situations. This data is particularly useful to automotive service providers such as car insurers, rental companies, and manufacturers. One way to understand how this data is used is to imagine the scenario in which an automobile insurer would provide a discount to a customer with an accident-free or near accident-free driving record. However, it is still possible that a less than the honest customer could manipulate their driving data in order to receive premium insurance services at preferential rates. To prevent this and similar scenarios, it is then critical to ensure that all data generated in a vehicle upholds integrity, continuity, and non-repudiation. Unfortunately, no such trustworthy data recording system of this caliber exists in any manufactured vehicle to date. This paper attempts to respond to this need, and we present a reliable automotive data recording system that satisfies these requirements and detects malicious manipulations from data deletion, replacement, replaying, and truncation. The proposed method additionally satisfies forward integrity of message authentication keys and is designed to utilize recorded data as automotive forensic evidence. Finally, the evaluation results demonstrate that our system can manage bandwidths of up to 64 MB/s.

Original languageEnglish
Article number8689029
Pages (from-to)49738-49755
Number of pages18
JournalIEEE Access
Publication statusPublished - 2019


  • ARM TrustZone
  • Forward integrity
  • audit trail
  • digital forensics
  • event data recorder

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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