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Presentation
Detection of Injection Attacks in In-Vehicle Networks
Electrical and Computer Engineering Conference Papers, Posters and Presentations
  • Lotfi ben Othmane, Iowa State University
  • Lalitha Dhulipala, Iowa State University
  • Moataz Abdelkhalek, Iowa State University
  • Manimaran Govindarasu, Iowa State University
  • Nicholas Multari, Pacific Northwest National Laboratories
Document Type
Conference Proceeding
Publication Version
Submitted Manuscript
Publication Date
1-1-2019
Abstract

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this paper, using injected speed reading and Revolutions Per Minute (RPM) messages, we examined the ability of the Pearson correlation, the k-means clustering, and the Hidden Markov Model (HMM) techniques to differentiate ’no-attack’ and ’under-attack’ states of the given vehicle. We found that the Pearson correlation distinguishes the two states while the k-means fails to distinguish the two states and HMM can successfully detect attacks but may have a high false positive rate. In addition, we found that the HMM-based detection method, and the k-means clustering methods exhibit different capabilities to detect attacks on the speedometer and tachometer sensors. The results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.

Comments

This is a draft manuscript of the article Ben Othmane, Lotfi, Lalitha Dhulipala, Moataz Abdelkhalek, Manimaran Govindarasu, and Nicholas Multari. "Detection of Injection Attacks in In-Vehicle Networks." (2019).

Copyright Owner
The Authors
Language
en
File Format
application/pdf
Citation Information
Lotfi ben Othmane, Lalitha Dhulipala, Moataz Abdelkhalek, Manimaran Govindarasu, et al.. "Detection of Injection Attacks in In-Vehicle Networks" (2019)
Available at: http://works.bepress.com/lotfi-benothmane/5/