Leaking your engine speed by spectrum analysis of real-Time scheduling sequences

Songran Liu, Nan Guan, Dong Ji, Weichen Liu, Xue Liu, Wang Yi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


This paper identifies and studies a new security/privacy issue for automobile vehicles. Specifically, attackers can infer the engine speed of a vehicle by observing and analyzing the real-time scheduling sequences on the Engine Control Unit (ECU). First, we present the problem model of engine-triggered task executed on ECU. And then, we introduce two Engine-triggered Task Period Tracing methods (DFT-based ETPT and FRSP-based ETPT) to infer the period variation of engine-triggered task. Finally, simulation experiments are conducted to demonstrate the effect of this new timing side-channel information leakage with our proposed methods.

Original languageEnglish
Pages (from-to)455-466
Number of pages12
JournalJournal of Systems Architecture
Publication statusPublished - Aug 2019


  • Real-time system
  • Scheduling sequences
  • Signal processing

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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