Advanced Analytics for Connected Car Cybersecurity

Matan Levi, Yair Allouche, Aryeh Kontorovich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Scopus citations

Abstract

The vehicular connectivity revolution is fueling the automotive industry's most significant transformation seen in decades. However, as modern vehicles become more connected, they also become much more vulnerable to cyber-attacks. In this paper, a fully working machine learning approach is proposed to protect connected vehicles (fleets and individuals) against such attacks. We present a system that monitors different vehicle interfaces (Network, CAN, and OS), extracts relevant information based on configurable rules, and sends it to a trained generative model to detect deviations from normal behavior. Using a configurable data collector, we provide a higher level of data abstraction as the model is trained based on events instead of raw data, which has a noise-filtering effect and eliminates the need to retrain the model whenever a protocol changes. We present a new approach for detecting anomalies, tailored to the temporal nature of our domain. Adapting a hybrid approach to the fully temporal setting, we first train a Hidden Markov Model to learn normal vehicle behavior, and then a regression model to calibrate the likelihood threshold for anomaly. Using this architecture, our method detects sophisticated and realistic anomalies, which are missed by other existing methods monitoring the CAN bus only. We also demonstrate the superiority of adaptive thresholds over static ones. Furthermore, our approach scales efficiently from monitoring individual cars to serving large fleets. We demonstrate the competitive advantage of our model via encouraging empirical results.

Original languageEnglish
Title of host publication2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-7
Number of pages7
Volume2018-June
ISBN (Electronic)9781538663554
DOIs
StatePublished - 20 Jul 2018
Event87th IEEE Vehicular Technology Conference, VTC Spring 2018 - Porto, Portugal
Duration: 3 Jun 20186 Jun 2018

Conference

Conference87th IEEE Vehicular Technology Conference, VTC Spring 2018
Country/TerritoryPortugal
CityPorto
Period3/06/186/06/18

Keywords

  • Anomaly detection
  • Connected cars
  • Hidden Markov models
  • Intrusion detection
  • Linear regression
  • Vehicle Cybersecurity

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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