Brief industry paper: HDAD: hyperdimensional computing-based anomaly detection for automotive sensor attacks

Ruixuan Wang, Fanxin Kong, Hasshi Sudler, Xun Jiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

As the connectivity of autonomous vehicles keeps growing, it is an accepted fact that they are even more vulnerable to malicious cyber-attacks. Recently, sensor spoofing has become an emerging attack that can compromise vehicle safety as vehicles are equipped with more sensors. Thus, it is critical to validate the sensor readings before utilizing them for future actions. In this paper, we develop HDAD, a hyperdimensional computing-based anomaly detection method. Hyperdimensional computing (HDC) is an emerging brain-inspired computing paradigm that mimics the brain cognition and leverages hyperdimensional vectors with fully distributed holographic representation and (pseudo)randomness. The key idea of HDAD is to use HDC to build encoder and decoder to reconstruct the sensor readings. The anomalous data typically have comparatively higher reconstruction errors than normal sensor readings. We explore three different metrics to measure the reconstruction error including mean squared error, mean absolute error, and cosine similarity. Using a real-world vehicle sensor reading dataset, we demonstrate the feasibility and efficacy of HDAD, opening the door for a new set of anomaly detection algorithm design.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium, RTAS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages461-464
Number of pages4
ISBN (Electronic)9781665403863
DOIs
StatePublished - May 2021
Event27th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2021 - Virtual, Online
Duration: May 18 2021May 21 2021

Publication series

NameProceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS
Volume2021-May
ISSN (Print)1545-3421

Conference

Conference27th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2021
CityVirtual, Online
Period5/18/215/21/21

Keywords

  • anomaly detection
  • brain inspired computing
  • hyperdimensional computing

ASJC Scopus subject areas

  • Engineering(all)

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