English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recently, cyberattacks on Controller Area Network (CAN) which is one of the automotive networks are becoming a severe problem. CAN is a protocol for communicating among Electronic Control Units (ECUs) and it is a de-facto standard of automotive networks. Some security researchers point out several vulnerabilities in CAN such as unable to distinguish spoofing messages due to no authentication and no sender identification. To prevent a malicious message injection, at least we should identify the malicious senders by analyzing live messages. In previous work, a delay-time based method called Divider to identify the sender node has been proposed. However, Divider could not identify ECUs which have similar variations because Divider's measurement clock has coarse time-resolution. In addition, Divider cannot adapt a drift of delay-time caused by the temperature drift at the ambient buses. In this paper, we propose a super fine delay-time based sender identification method with Time-to-Digital Converter (TDC). The proposed method achieves an accuracy rate of 99.67% in the CAN bus prototype and 97.04% in a real-vehicle. Besides, in an environment of drifting temperature, the proposed method can achieve a mean accuracy of over 99%.

PLI-TDC: Super Fine Delay-Time Based Physical-Layer Identification with Time-to-Digital Converter for In-Vehicle Networks